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Background: Despite a decline in global adolescent birth rate, many countries in South East Asia still experience a slower pace decline in adolescent birth rates. Timor-Leste is one of the countries in the region with the highest adolescent birth rate and huge disparities between socio-economic subgroups. Hence, this study assessed the magnitude and trends in adolescent fertility rates within different socio-demographic subgroups in Timor-Leste. Methods: Using the World Health Organization’s (WHO) Health Equity Assessment Toolkit (HEAT) software, data from the Timor-Leste Demographic and Health surveys (TLDHS) were analyzed between 2009 and 2016. We approached the inequality analysis in two steps. First, we disaggregated adolescent fertility rates by four equity stratifiers: wealth index, education, residence and region. Second, we measured the inequality through summary measures, namely Difference, Population Attributable Risk, Ratio and Population Attributable Fraction. A 95% confidence interval was constructed for point estimates to measure statistical significance. Results: We found large socio-economic and area-based inequalities over the last 7 years. Adolescent girls who were poor (Population Attributable Fraction: -54.87, 95% CI; − 57.73, − 52.02; Population Attributable Risk: -24.25, 95% CI; − 25.51, − 22.99), uneducated (Difference: 58.69, 95% CI; 31.19, 86.18; Population Attributable Fraction: -25.83, 95% CI; − 26.93, − 24.74), from rural areas (Ratio: 2.76, 95% CI; 1.91, 3.60; Population Attributable Risk: -23.10, 95% CI; − 24.12, − 22.09) and from the Oecussi region (Population Attributable Fraction: -53.37, 95% CI; − 56.07, − 50.67; Difference: 60.49, 95% CI; 29.57, 91.41) had higher chance of having more births than those who were rich, educated, urban residents and from the Dili region, respectively. Conclusions: This study identified disproportionately higher burden of teenage birth among disadvantaged adolescents who are, poor, uneducated, rural residents and those living in regions such as Oecussi, Liquica and Manufahi, respectively. Policymakers should work to prevent child marriage and early fertility to ensure continuous education, reproductive health care and livelihood opportunities for adolescent girls. Specialized interventions should also be drawn to the subpopulation that had disproportionately higher adolescent childbirth.
Timor-Leste is one of the lower-middle-income countries in Southeast Asia and has a population density of 1.2 million that is growing at 3.2% per year [21]. Majority of the population in the country live in rural areas (74%) and are engaged in small-scale subsistence farmers [22]. In line with its ethnic mix of colonial history [23], the country has twelve ethnic groups [23], with thirty-two [24] number of languages spoken, of which Tetum and Portuguese remain official languages, while Indonesian and English are working languages [25, 26]. Oil is the major source of revenue for the country and it accounted for more than 30% of the country’s total revenue in 2016 [27]. Between 2007 and 2012, there was fluctuations in headline economic growth rates leading to more than doubling in GNI per capita. This was due to variations in oil prices and falling production. However, the economic growth dropped back to its 2007 level by 2016. Non-oil GDP per capita has risen steadily, growing an average 5% a year from 2006 to 2016 when it stood at US$1336 per person [27]. In 2018, gross national income was US$ 7658 per capita and gross domestic product growth was 4% per year (dollars are valued at purchasing power parity [21]. The discovery of oil brought advancement in the economy and improved capital development, including reinforcement of the health care system [28, 29]. Consequently, the healthcare system in Timor-Leste is operated by the public sector through public funding. In the country, there is cost-free health services provision at the point of use, through the large proportionate government contributions to expenditure on health care (90% of total health care expenditure) [28]. The country operates a three-tier health care delivery system, in which the National Hospital in Dili (the capital) provides tertiary care, 5 referral hospitals at the district level provide secondary services, and a network of 66 community health centers (CHCs) and 205 health posts deliver primary health care (PHC) services across the country’s 13 districts. In addition, the CHCs carry out special monthly outreach programmes known locally as Servisu Integrado du Saude Comunidade (SISCa) [30]. To ensure adequate access to healthcare services, health services have been designed in such a way that everyone should have access to them within a 1hour walking distance. The private health system remains relatively underdeveloped, although the Ministry of Health (MoH) estimates that about 25% of basic health services are delivered by private providers (both for profit and non-profit) [30]. The Demographic and Health Surveys conducted in Timor-Leste in 2009 and 2016 were used as the sources of data for this study. Briefly, a two-stage cluster design was employed to select women aged 15 to 49 years, men aged between 15 to 59 years, and children. The DHS surveys are nationally representative and collect data on a wide range of public health related topics and indicators such as maternal health services, child health, maternal and childhood mortality, socioeconomic status, family planning and domestic violence. These surveys were carried out with the financial and technical assistance of Inner-City Fund (ICF) International and provisioned through the USAID-funded MEASURE DHS program. The survey also enrolled adolescent girls aged 15 to 19 years. The detailed methodology of the TDHS is available in the surveys’ final reports [24, 31]. The inequality variable measured in this study is adolescent fertility rate (AFR). It is measured as proportion of births per 1000 women aged 15–19. We disaggregated the AFR by four equity stratifiers: economic status, education, subnational region and place of residence. We approximated economic status through a composite variable known as wealth index. In the DHS, wealth index is computed using different household ownerships and characteristics following Principal Component Analysis (PCA) technique [32]. Wealth index has five categories: poorest, poor, middle, rich and richest. Educational status of the woman was classified as no-education, primary and secondary/higher and place of residence as urban versus rural. The disaggregated AFR (reported as percentage) was presented for each of the four Timor-Leste Demographic and Health Surveys (TLDHS) time periods. The educational status and wealth index have a natural ordering and are known as ordered equity stratifiers whereas region is a non-ordered equity stratifier. Whether an equity stratifier is ordered or not, affects the choice of summary measures to be calculated [33]. We used the 2019 updated version of the World Health Organization’s (WHO) Health Equity Assessment Toolkit (HEAT) software [33] for analyzing the socioeconomic and area-based inequalities associated with AFR. Detailed description of the software has been given elsewhere [33]. The WHO released the software in 2016 through the use of free and publicly available R programming language and the R packages. The software provides the opportunity to do assessment of within country health inequalities of more than 30 indicators for reproductive, maternal, newborn, and child health (RMNCH). It also creates room for benchmarking inequality in one country with that of another country, allowing for direct comparison of inequality in two or more countries at the same time [33, 34]. The type of health indicator of interest (favorable versus adverse) and the inherent properties of dimensions of inequality determined the choice and interpretations of summary measures for this study. We computed the summary measures with the mindset that AFR is an adverse health indicator. We carried out our data analyses using a two-step approach. First, we disaggregated AFR by the commonly used dimensions of inequality (economic status, education, subnational region and place of residence). Then, we calculated summary measures and used a combination of absolute and relative inequality summary measures. These were Difference (D), Population Attributable Risk (PAR), Population Attributable Fraction (PAF) and Ratio (R). The first two (D and PAR) are absolute inequality measures while the other two (PAF and R) are relative measures of inequality. These measures were calculated for each of the four equity stratifiers. The detailed methods of calculation, interpretation and all other detailed properties of the measures employed in the study have been described elsewhere in detail [33]. The R and D are simple measures suitable for determining the relative ratio and absolute difference between two categories within a dimension of inequality (i.e. urban versus rural for residence) or between 2 or more categories (i.e. regions) based on an identified reference subgroup in the category. PAR and PAF are weighted complex measures of inequality that consider the sizes of subpopulations used in the calculation, hence producing estimates reflective of the subpopulation size [33, 34]. Complex measures are the ones that take into consideration the sizes of the subpopulation used in the calculation of a measure in question, thereby producing estimates reflective of size of the subpopulation [33, 34]. The PAF and PAR take positive values for favorable health intervention indicators and negative values for adverse health outcome indicators like AFR. Zero shows absence of inequality and the greater absolute value of PAF and PAR indicates a higher level of inequality. PAR is calculated as the difference between the subgroup with the lowest estimate and the national average of the indicator for adverse outcome indicators. For ordered dimensions like wealth and education, PAR is the difference between the most-advantaged subgroup and the national average, regardless of the indicator type. PAF is calculated by dividing the PAR by the national average μ and multiplying the fraction by 100: PAF = [PAR / μ] * 100. For binary dimensions like residence, difference is calculated as the difference between the subgroup with the highest estimate (rural) and the subgroup with the lowest estimate (urban), regardless of the indicator type. For ordered dimensions like wealth and education, it is the difference between the most-disadvantaged subgroup and the most advantaged subgroup. For binary dimensions like residence, ratio is calculated as the difference between the subgroup with the highest estimate (rural) and the subgroup with the lowest estimate (urban), regardless of the indicator type. For ordered dimensions like wealth and education, it is the Ratio between the most-disadvantaged subgroup and the most advantaged subgroup. In the absence of inequality, Difference and Ratio become zero and one, respectively. Point estimates were calculated and presented with corresponding 95% Confidence Intervals. To examine whether AFR shows statistically significant disparities across the sub-groups of each equity stratifier, and to determine whether or not the inequality changed with time, we computed 95% Confidence Intervals (CI) around point estimates of each measure for each survey. For all inequality measures other than Ratio, the lower and upper bounds of the CI must not include zero to interpret that inequality exists. For Ratio, the interval should not include one. We assessed the trend of inequality for each summary measure by referring to the CIs for the different survey years; if the CIs did not overlap, inequality existed. This study used publicly available data stored in the HEAT software application. Informed consent for the TDHS that were available in the HEAT software was obtained from participants prior to the survey. The DHS Program follows ethical standards for ensuring the protection of respondents’ privacy. ICF International ensures that the survey complies with the U.S. Department of Health and Human Services regulations for the respect of the right of human subjects. No further approval was required for this study since the data is secondary and available in the public domain. More details about DHS data and ethical standards are available at: https://bit.ly/2XHjJsR
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