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      Prevalence and determinants of preterm birth among women of reproductive age in Kenya: a multilevel analysis of the 2022 Demographic Health Survey

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          Abstract

          Background:

          Globally, over 15 million preterm births (PTB) occur annually, with sub-Saharan Africa bearing a disproportionate burden. In Kenya, studies conducted between 2017 and 2021 at the hospital level show a PTB prevalence ranging from 15.9% to 20.2%. However, current PTB prevalence and associated factors remain underexplored despite their significant public health implications. Understanding the prevalence and factors associated with PTB is critical for effective interventions.

          Objectives:

          This study aimed to determine the prevalence of PTB and also to identify individual- and community-level factors influencing PTB among women of reproductive age in Kenya.

          Design:

          The study utilised a cross-sectional design, analysing data from the 2022 Kenya Demographic and Health Survey.

          Methods:

          A sample of 7291 women aged 15–49 was analysed using weighted multilevel logistic regression in Stata 17.0. Adjusted odds ratios (aOR) with 95% confidence intervals (CI) and a significance threshold of p < 0.05 were used to identify predictors of PTB.

          Results:

          The prevalence of PTB was 7.14%. Women aged 25–34 (aOR = 0.67; 95% CI: 0.49–0.94) and 35+ (aOR = 0.86; 95% CI: 0.59–1.24) were less likely to experience PTB compared to younger women (15–24 years). Attending four or more antenatal care visits reduced PTB likelihood (aOR = 0.68; 95% CI: 0.53–0.88). Women in the richest wealth index had higher odds of PTB (aOR = 2.28; 95% CI: 1.39–3.74), while medium community literacy levels increased PTB risk (aOR = 1.56; 95% CI: 1.21–2.03).

          Conclusion:

          This study highlights that individual- and community-level factors significantly influence PTB in Kenya. Addressing disparities in socio-demographic and obstetric factors through targeted, multipronged strategies is essential for reducing PTB rates and improving maternal and neonatal outcomes.

          Plain Language Summary

          Preterm births in Kenya: how common are they and what factors contribute

          Preterm birth (PTB) is when a baby is born before 37 weeks of pregnancy. It’s a major public health issue, with over 15 million cases globally each year. Sub-Saharan Africa, including Kenya, has a high share of PTB, but there’s limited research about it in Kenya. This study looked at how common PTB is in Kenya and what factors increase the risk. Using data from the 2022 Kenya Demographic and Health Survey, the authors analysed 7,291 women aged 15–49. They used statistical methods to find patterns and identify factors linked to PTB. The study found that 7.14% of women in the sample had a preterm birth. Younger women aged 15–24 were more likely to experience PTB compared to women aged 25–34 or 35 and older. Women who attended at least four antenatal care visits were less likely to have a preterm birth. Surprisingly, women from the richest households had a higher risk of PTB, and living in communities with medium levels of literacy also increased the risk. The findings show that both personal and community factors affect the chances of preterm birth. To reduce PTB rates, efforts should focus on improving access to antenatal care, addressing social and economic inequalities, and promoting education at the community level. These steps can help improve the health of mothers and babies in Kenya.

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          Most cited references59

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          National, regional, and worldwide estimates of preterm birth rates in the year 2010 with time trends since 1990 for selected countries: a systematic analysis and implications.

          Preterm birth is the second largest direct cause of child deaths in children younger than 5 years. Yet, data regarding preterm birth (<37 completed weeks of gestation) are not routinely collected by UN agencies, and no systematic country estimates nor time trend analyses have been done. We report worldwide, regional, and national estimates of preterm birth rates for 184 countries in 2010 with time trends for selected countries, and provide a quantitative assessment of the uncertainty surrounding these estimates. We assessed various data sources according to prespecified inclusion criteria. National Registries (563 datapoints, 51 countries), Reproductive Health Surveys (13 datapoints, eight countries), and studies identified through systematic searches and unpublished data (162 datapoints, 40 countries) were included. 55 countries submitted additional data during WHO's country consultation process. For 13 countries with adequate quality and quantity of data, we estimated preterm birth rates using country-level loess regression for 2010. For 171 countries, two regional multilevel statistical models were developed to estimate preterm birth rates for 2010. We estimated time trends from 1990 to 2010 for 65 countries with reliable time trend data and more than 10,000 livebirths per year. We calculated uncertainty ranges for all countries. In 2010, an estimated 14·9 million babies (uncertainty range 12·3-18·1 million) were born preterm, 11·1% of all livebirths worldwide, ranging from about 5% in several European countries to 18% in some African countries. More than 60% of preterm babies were born in south Asia and sub-Saharan Africa, where 52% of the global livebirths occur. Preterm birth also affects rich countries, for example, USA has high rates and is one of the ten countries with the highest numbers of preterm births. Of the 65 countries with estimated time trends, only three (Croatia, Ecuador, and Estonia), had reduced preterm birth rates 1990-2010. The burden of preterm birth is substantial and is increasing in those regions with reliable data. Improved recording of all pregnancy outcomes and standard application of preterm definitions is important. We recommend the addition of a data-quality indicator of the per cent of all live preterm births that are under 28 weeks' gestation. Distinguishing preterm births that are spontaneous from those that are provider-initiated is important to monitor trends associated with increased caesarean sections. Rapid scale up of basic interventions could accelerate progress towards Millennium Development Goal 4 for child survival and beyond. Bill & Melinda Gates Foundation through grants to Child Health Epidemiology Reference Group (CHERG) and Save the Children's Saving Newborn Lives programme; March of Dimes; the Partnership for Maternal Newborn and Childe Health; and WHO, Department of Reproductive Health and Research. Copyright © 2012 Elsevier Ltd. All rights reserved.
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            Data Analysis Using Regression and Multilevel/Hierarchical Models

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              Demographic and health surveys: a profile.

              Demographic and Health Surveys (DHS) are comparable nationally representative household surveys that have been conducted in more than 85 countries worldwide since 1984. The DHS were initially designed to expand on demographic, fertility and family planning data collected in the World Fertility Surveys and Contraceptive Prevalence Surveys, and continue to provide an important resource for the monitoring of vital statistics and population health indicators in low- and middle-income countries. The DHS collect a wide range of objective and self-reported data with a strong focus on indicators of fertility, reproductive health, maternal and child health, mortality, nutrition and self-reported health behaviours among adults. Key advantages of the DHS include high response rates, national coverage, high quality interviewer training, standardized data collection procedures across countries and consistent content over time, allowing comparability across populations cross-sectionally and over time. Data from DHS facilitate epidemiological research focused on monitoring of prevalence, trends and inequalities. A variety of robust observational data analysis methods have been used, including cross-sectional designs, repeated cross-sectional designs, spatial and multilevel analyses, intra-household designs and cross-comparative analyses. In this profile, we present an overview of the DHS along with an introduction to the potential scope for these data in contributing to the field of micro- and macro-epidemiology. DHS datasets are available for researchers through MEASURE DHS at www.measuredhs.com.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: InvestigationRole: Project administrationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: SoftwareRole: SupervisionRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: InvestigationRole: Writing – original draftRole: Writing – review & editing
                Role: InvestigationRole: SoftwareRole: ValidationRole: Visualization
                Role: InvestigationRole: Writing – original draftRole: Writing – review & editing
                Role: InvestigationRole: Writing – original draftRole: Writing – review & editing
                Role: InvestigationRole: Writing – original draftRole: Writing – review & editing
                Journal
                Ther Adv Reprod Health
                Ther Adv Reprod Health
                REH
                spreh
                Therapeutic Advances in Reproductive Health
                SAGE Publications (Sage UK: London, England )
                2633-4941
                20 March 2025
                Jan-Dec 2025
                : 19
                : 26334941251327181
                Affiliations
                [1-26334941251327181]Center for Community Health and Aging, Department of Health Behavior, School of Public Health, Texas A&M University, College Station, TX, USA
                [2-26334941251327181]Department of Public Health, York St John University, 1 Clove Street, East India, London E14 2BA, UK
                [3-26334941251327181]Department of Demography and Population Studies, University of Witwatersrand, Johannesburg, South Africa
                [4-26334941251327181]Public Health Department, Calvin University, Grand Rapids, MI, USA
                [5-26334941251327181]Department of Sociology and Criminology & Law, University of Florida, Gainesville, FL, USA
                [6-26334941251327181]Department of Epidemiology, University of Florida, Gainesville, FL, USA
                [7-26334941251327181]Department of Public Health Medicine, University of KwaZulu-Natal, Durban, South Africa
                [8-26334941251327181]Kent County Council, Maidstone, Kent ME14 1XQ, UK
                Author notes
                Author information
                https://orcid.org/0000-0002-9208-6408
                https://orcid.org/0000-0002-2150-5177
                https://orcid.org/0000-0001-6313-9251
                Article
                10.1177_26334941251327181
                10.1177/26334941251327181
                11926847
                d7d7a63f-47e6-4616-910c-0c65a04ebb19
                © The Author(s), 2025

                This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License ( https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages ( https://us.sagepub.com/en-us/nam/open-access-at-sage).

                History
                : 15 November 2024
                : 24 February 2025
                Categories
                Original Research
                Custom metadata
                January-December 2025
                ts1

                kdhs,kenya,maternal health,multilevel analysis,preterm birth,prevalence,sub-saharan africa

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