Background: Research demonstrates that coaching is an effective method for promoting behavior change, yet little is known about which attributes of a coach make them more or less effective. Thispost hoc, sub-analysis of the BetterBirth trial used observational data to explore whether specific coaches’ and team leaders' characteristics were associated with improved adherence to essential birth practices listed on the World Health Organization Safe Childbirth Checklist.Methods: A descriptive analysis was conducted on the coach characteristics from the 50 BetterBirth coaches and team leaders. Data on adherence to essential birth practices by birth attendants who received coaching were collected by independent observers. Bivariate linear regression models were constructed, accounting for clustering by site, to examine the association between coach characteristics and attendants’ adherence to practices. Results: All of the coaches were female and the majority were nurses. Team leaders were comprised of both males and females; half had clinical backgrounds. There was no association between coaches’ or team leaders’ characteristics, namely gender, type of degree, or years of clinical training, and attendants’ adherence to essential birth practices. However, a significant inverse relationship was detected between the coach or team leader’s age and years of experience and the birth attendants’ adherence to the checklist. Conclusion: Younger, less experienced coaches were more successful in promoting essential birth practices adherence in this population. More data is needed to fully understand the relationship between coaches and birth attendants. Coaching, which differs from clinical training, focuses on strategically supporting someone as they determine how to put knowledge into practice. Among clinicians, there is often a disconnect between theoretical knowledge and practical application of clinical skills3. Employing a coaching-based approach can be a critical method to support clinicians’ progress from beginner to expert in assessment and intervention decisions8.Coaching is a successful strategy for changing individuals' performance and behaviors within industries such as sportspost hoc, sub-analysis of the BetterBirth trial used observational data to explore whether specific coaches’ and team leaders’ characteristics were associated with improved adherence by birth attendants to essential birth practices (EBPs) listed on the WHO Safe Childbirth Checklist (SCC)9.Although the literature demonstrates that coaching is an effective method for promoting behavior change, little is known about which coach attributes are effective. This9. The original trial took place in 120 facilities across 24 districts of Uttar Pradesh9. At each intervention facility (n=60) and its matched control site (n=60), patients were enrolled two months after the launch of the coaching intervention; health outcomes of women and their newborns were collected at 7 days postpartum9.The BetterBirth Trial, a large cluster-randomized controlled trial conducted in Uttar Pradesh, India, demonstrated that a coaching-based implementation of the SCC increased birth attendants’ adherence to EBPs, but had no effect on maternal/perinatal health outcomes4. Team leaders attended every other visit with coaches to provide supportive supervision. In a sub-set of 30 facilities, independent observers (neither coaches nor staff) documented birth attendant’s adherence to practices. Observers recorded data on all practices within a specific time frame ; deliveries were observed for 1 or more pause points. Observation of practices was limited to practical and observable interactions between provider and patient or provider actions to ready supplies9. Data collection took place 2 months and 6 months after coaching. Only independent observer data from intervention facilities (15 facilities) are included in this analysis, as control facilities did not receive coaching.Intervention sites were assigned coaches for 8 months to empower birth attendants to identify and resolve the barriers they faced while using the SCC10.Using data collected during the main trial, we conducted a descriptive analysis on the coach characteristics and the relationship of those attributes and adherence to the behaviors on the WHO Safe Childbirth Checklist. We included information from the 50 BetterBirth coaches and team leaders; a subset of which (n=17 coaches or team leaders) provided coaching at facilities where independent observation was completed. Using data from the study hiring database, the variables on coaches/team leaders’ gender, age, professional degree, years of clinical training and years of experience were used in the analysis. The roles of coach and team leaders for the analysis were based on the individual’s initial role when the trial started as some coaches graduated to team leader roles throughout the trial. All coaches and team leaders were included in the descriptive analysis; only the coaches and team leaders in the subset of facilities with independent observer data were included in the subsequent models. STROBE reporting guidelines were used in the submission of this study11. Separate models were created for coaches and team leaders. Practice adherence data was collected by independent observers. Practice adherence was calculated as a summary score of the 18 EBPs for each birth and each practice was weighted equally. The EBP score was then assigned to each coach who visited the facility where the observation took place, as coaches attempted to provide at least one coaching session to every birth attendant at a facility. The parameter estimates for the model can be interpreted as the difference in mean EBP scores between the different levels of demographic characteristics in the study. All statistical analyses were performed inSAS version 9.4 .Generalized linear models were constructed, accounting for clustering of births within site, to examine the association between coach characteristics and birth attendants’ adherence to EBPs. The models were estimated using generalized estimating equations12). The full complement of coaches and team leaders were relatively similar to the subset of coaches working at facilities where independent observations occurred. The only difference is the proportion of males in the team leader group (63%) and males in the team leader analytic dataset (29%).All coaches were female and the majority were nurses. Team leaders were comprised of both males and females; half of which had clinical backgrounds 12. The 12). As the coach’s age increased by 10 years, the mean summary score of the 18 EBP adherence decreased by almost one checklist item (ϐ = -0.93). Similarly, as the coach’s years of experience increased the number of EBP decreased slightly (ϐ = -0.13). Similar effects were found for team leaders.There was no association between coach’s or team leaders’ gender, type of degree, or years of clinical training and providers’ adherence to EBPs. However, a significant inverse relationship was detected between the coach’s or team leader’s age as well as years of experience and the birth attendants’ adherence to the checklist 12. As tThe inverse relationship between the coaches’ age and experience and adherence to EBPs suggests that younger, less experienced coaches were more successful in promoting practice adherence. Younger coaches may have been less directive, especially when coaching birth attendants who were older and/or more experienced. Additionally, coaches and team leaders possess various learning styles; the coach foundation training may have been absorbed differently by each.4. While the number of coaches included in the analytic dataset is small, the total number of observations of care was substantial.A potential limitation is the introduction of bias by independent observers. To mitigate bias, observers completed standardized training (with six-month refreshers), which included procedures for using the observation tool in practice and definitions for analyzing EBP adherence. For practical reasons, these observations were performed at nonrandomly selected sites during daytime hours, which potentially limits generalizability to unobserved births13. One survey of nearly 300 individuals in 34 different countries conducted by the Institute for Employment Studies found that factors such as age and gender of their coach were less important to coachees14. The most important quality of a coach to a coachee was that the coach displayed acceptance of the individual14. This matches our broader experience at Ariadne Labs, where we have often seen that softer skills  produce higher rates of sustained behavior change, but these factors are difficult to measure. Future research should include a mixed methods approach to explore how factors like personal styles, cultural dynamics, and hierarchy affect coaching content uptake.The notion that coaching has positive effects on individual behavior change outcomes is well-supported in the literature. However, a paucity of studies explore the various dynamics between coach and coachee relationships suggests that more information is needed to fully understand the relationship between coaches and birth attendantsAt trial initiation, birth attendants and facility staff provided written consent to participate. Before an independent observer collected data, the birth attendant verbally reconfirmed agreement; laboring women who were observed provided written consent. Electronic data were deidentified and stored in a Health Insurance Portability and Accountability Act–compliant database to ensure participant privacy. In directly observed births, women or their surrogates provided written consent for observation. The study protocol was approved by the Community Empowerment Lab (CEL) Ethics Review Committee (Ref no: 2014006), Jawaharlal Nehru Medical College Ethical Review Committee (Ref no: MDC/IECHSR/2015-16/A-53), the Institutional Review Board of the Harvard T.H. Chan School of Public Health (Protocol 21975-102), the Population Services International Research Ethics Board (Protocol ID: 47.2012), and the Ethical Review Committee of the World Health Organization (Protocol ID: RPC 501), and the Indian Council of Medical Research. The protocol was reviewed and reapproved on an annual basis.https://doi.org/10.7910/DVN/BCMETW12Harvard Dataverse: Who's Your Coach? The relationship between coach characteristics and birth attendants' adherence to the WHO Safe Childbirth Checklist.This project contains the following underlying data:▪ coachtl_chars.sas7bdat (SAS dataset with demographic characteristics of coach team leaders)▪ coachtl_checklist.sas7bdat (SAS dataset with adherence to checklist behaviors in coach team leader facilities)▪ Coach_characteristics_DataDictionary.tab ▪ coach_chars.sas7bdat (SAS dataset with demographic characteristics of coaches)▪ coach_checklist.sas7bdat (SAS dataset with adherence to checklist behaviors in coached facilities)▪ Coach_CoachTL_Checklist_DataDictionary.tab Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication).Data are available under the terms of the This is a well written paper which shines the light on an important topic: the characteristics of a successful coach. Given the potential investments of the government in hriing of Quality Mentors, the guidance provided by this paper can inform the selection process. I would recommend clearly describing and differentiating the role of the coach versus a Team Leader. While the role of coach is clear, the concept of team leader is not- who is the team leader; who do they lead, what is their role? In the abstract, authors mention the use of Bivariate analysis. But in the methods section of the main paper, multiple background characteristics have been mentioned as variables. Any reason why a multi-variate analysis was not attempted?  There is a speculation made in the conclusion related to effect of training of coaches on their effectiveness.Were proficiency levels of coaches tested at any stage? It would have helpful to have that analysis.Is the work clearly and accurately presented and does it cite the current literature?YesIf applicable, is the statistical analysis and its interpretation appropriate?PartlyAre all the source data underlying the results available to ensure full reproducibility?YesIs the study design appropriate and is the work technically sound?PartlyAre the conclusions drawn adequately supported by the results?YesAre sufficient details of methods and analysis provided to allow replication by others?YesReviewer Expertise:Reproductive, Maternal, Neonatal Health; Women's Cancers; Health Systems/Quality of CareI confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. 1. The WHO Safe Childbirth Checklist aims to help frontline healthcare workers prevent avoidable childbirth-related mortality/morbidity as seen in sub-Saharan Africa2.The Safe Childbirth Checklist is a tool recently introduced by the World Health Organization (WHO) to reduce maternal morbidity and mortality3 is a multi-authored study on the characteristics  and effectiveness of a coach with respect to adherence to essential birth practices by birth attendants who were coached. The authors present a sub-analysis of the BetterBirth trial using observational data to explore whether specific coaches’ and team leaders' characteristics were associated with improved adherence to essential birth practices listed on the WHO Safe Childbirth Checklist.  “Who’s your coach? The relationship between coach characteristics and birth attendants’ adherence to the WHO Safe Childbirth Checklist" The Introduction clearly demonstrates that coaching is an effective method, yet little is known about the attributes or characteristics of a coach that make them more or less effective for promoting behavior change.  A descriptive analysis of the coach characteristics from the 50 BetterBirth coaches and team leaders formed the manuscript basis. Independent observers collected data on adherence to essential birth practices by birth attendants who received coaching. Bivariate linear regression models were constructed to examine the association between coach characteristics and attendants’ adherence to practices.  The results showed no association between the Coaches gender, type of degree, or years of clinical training, and attendants’ adherence to essential birth practices. However, a significant inverse relationship was detected between the coach age and years of experience and the birth attendants’ adherence to the checklist.  The authors concluded that younger, less experienced coaches were more successful in promoting essential birth practices adherence in this population and more data is needed to fully understand the relationship between coaches and birth attendants. The conclusions drawn are adequately supported by the results and the limitations were discussed. The Conclusion may be re-titled Discussion and Conclusion. This is a well thought out study that provides information necessary to promote behavior change. The statistical analysis and interpretation are appropriate. The work is clearly presented and cites current and relevant literature. The study design is appropriate and the work is technically sound. Independent observers (neither coaches nor staff) documented the birth attendant’s adherence to practices. The details of methods and analysis have been provided to allow replication by others. The authors have adhered to the STROBE reporting guidelines and the source data underlying the results are available and archived with a digital object identifier.Is the work clearly and accurately presented and does it cite the current literature?YesIf applicable, is the statistical analysis and its interpretation appropriate?YesAre all the source data underlying the results available to ensure full reproducibility?YesIs the study design appropriate and is the work technically sound?YesAre the conclusions drawn adequately supported by the results?YesAre sufficient details of methods and analysis provided to allow replication by others?YesReviewer Expertise:Maternal HealthI confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Thank you for giving me the opportunity to review this research article. The authors talked briefly in the background section about coaching, however a more detailed literature review is needed in order to justify the gap in the literature this study is trying to address. More details about the study design are needed. It is unclear to the reader why the subset of 10 coaches and 7 team leaders was included in the final analysis. What happened to the remaining coaches and team leaders in table 1. The choice of methodology should be justified. Is this the best method to determine the attributes of a good coach? Or a mixed-method would be better? The weakness of the method and lack of a validated tool to measure coaching performance should be recognized. In addition, the choice of factors included in the model needs explanation. In the discussion section, the fact of having only female coaching should be explained. In the conclusion section, the sentences below are speculations and should be moved to the discussion section: "Younger coaches may have been less directive, especially when coaching birth attendants who were older and/or more experienced. Additionally, coaches and team leaders possess various learning styles; the coach foundation training may have been absorbed differently by each."Is the work clearly and accurately presented and does it cite the current literature?PartlyIf applicable, is the statistical analysis and its interpretation appropriate?I cannot comment. A qualified statistician is required.Are all the source data underlying the results available to ensure full reproducibility?YesIs the study design appropriate and is the work technically sound?PartlyAre the conclusions drawn adequately supported by the results?PartlyAre sufficient details of methods and analysis provided to allow replication by others?YesReviewer Expertise:Global health, Quality improvement, Maternal Health, Anesthesia, and Medical Education.I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.


Information on repeat adolescent birth remains scarce in sub-Sahara Africa. We investigated the prevalence and time trends in repeat adolescent birth in Uganda, and associated factors.We analyzed Uganda Demographic and Health Survey data of women age 20–24 years collected on 6 surveys (1988/89-2016) to estimate repeat adolescent birth (first live birth <18 years of age followed by another live birth(s) <20 years). Further, we estimated the wantedness of the second order birth and the prevalence of short birth intervals birth (<13 months) between the first and second such birth. On the 2016 survey, we examined factors associated with repeat adolescent birth using bivariate and multivariate modified Poisson regression.At the 1988/89 survey, 58.9% of women with first birth <18 years reported a repeat adolescent birth. This percentage increased to 66.8% in 2006  and thereafter declined to 55.6% by 2016 , nevertheless, no change occurred between 1988/89 and 2016 . Among women with repeat adolescent births, the mean number of live births by exact age 20 years (2.2 births) and prevalence of short birth intervals   did not change. Increasingly more women with repeat adolescent births preferred to have had the second child later, 22.5% in 1995 and 43.1% in 2016 . On the 2016 survey, women from poorer households and those of younger age at first birth were significantly more likely to report repeat adolescent birth.Following a first birth <18 years, more than half of the women report a repeat adolescent birth (<20 years), with no decline observed in 30 years. Increasingly more women wanted the second adolescent pregnancy later, highlighting the need to support adolescents with improved family planning services at each contact. Globally, adolescent childbearing remains a major public health concern most especially in the low- and middle-income countries (LMICs). Approximately 1 in 8 of the 140 million births annually occurs to adolescent women with 95% of these occurring in LMIC, and 23% in sub-Sahara Africa Reviewers' comments:Reviewer's Responses to QuestionsComments to the Author1. Is the manuscript technically sound, and do the data support the conclusions?The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: YesReviewer #2: Partly**********2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: YesReviewer #2: Yes**********3. Have the authors made all data underlying the findings in their manuscript fully available?PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.The Reviewer #1: YesReviewer #2: Yes**********4. Is the manuscript presented in an intelligible fashion and written in standard English?PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.Reviewer #1: YesReviewer #2: Yes**********5. Review Comments to the AuthorPlease use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. Reviewer #1: This manuscript quantitatively describes repeat adolescent pregnancies in Uganda over almost 30 years. This is a valuable analysis of a meaningful outcome in a vulnerable population, and certainly deserves publication. Please see comments below. Overall, the data (and other previous data) is looked at from many angles and it is important to clarify and highlight the differences in the different statistics offered – eg what is the denominator? is it a rate or a prevalence? etc. I applaud the authors for tackling this complicated subject and look forward to seeing the final publication.Major Comments:1. The authors discuss the lack of a standardized definition of repeat adolescent pregnancy as a challenge. However, they chose not to use the definitions previously used in the literature. Please explain the rationale for your definition and explain why the benefit of doing the analysis in this way was big enough to outweigh the drawback of lack of comparability with other reports. 2. A major advocacy point that emerges from this data is the need for every contact between healthcare workers and pregnant adolescents to be seen as an opportunity to prevent unwanted repeat adolescent birth. This is powerful and actionable. It is amazing that the overall rate of adolescent birth is falling over time, but the rate of repeat adolescent birth (arguably easier to target) is not.Minor Comments:1. There are some minor areas of awkward wording in English. Eg:a. Line 42 and 48: “More women with repeat adolescent births preferred to have…” consider "Many women with repeat adolescent births..." or "An increasing number of ...”b. Line 68-69: “yet, the way the sexual intercourse came about may be linked to sexual and gender based violence…” consider: “and many of these pregnancies are result of sexual and gender based violence"c. Line 318-19 “This qualitative research should understand this” consider: “This qualitative research should investigate this” or “This qualitative research should approach this”2. Line 57-60 wording is unclear. First half of sentence says no change, the second half says there has been a decline. Consider: “These levels have remained high in the last 15 years despite a decline in the age specific fertility rate for Uganda among this category of women, 195 births per 1,000 women in the 1990 Uganda Demographic and Health Survey (UDHS) to 132 births per 1,000 women at the 2016 survey.” Also, I think these numbers should be “per 1,000 women per year”.3. Line 79: There are other studies that look at adolescent births and then estimate repeat pregnancies (though not the primary outcome) eg: Parker AL, Parker DM, Zan BN, Min AM, Gilder ME, Ringringulu M, et al. Trends and birth outcomes in adolescent refugees and migrants on the Thailand-Myanmar border, 1986-2016: an observational study. Wellcome Open Research. 2018 May 21;3:62.4. Line 96: How were surveys conducted? How many languages? In person facilitation? This gives an idea of what into the data collection. Could use a reference if it is described elsewhere.5. Line 123-125: The decisions on these categories seem a little arbitrary but I don't know the religious dynamics in Uganda intimately. I would expect Baptist might be appropriate to group with Anglican and Penticostal under "Protestant"... Alternately, Anglican and Penticostal are quite different in some parts of the world (but maybe not in Uganda). This may be fine as is but it struck me as a bit odd. The “traditional” religion group seems like a potentially interesting cohort but it may have been very small numbers and therefore hard to look at.6. Line 127-129: these variables that were excluded due to potential reverse causality seem very interesting.a. Educational level at a certain point predates the pregnancy – for example, “no education” or “some primary school” or “completed primary school” could be interesting variables that would be expected to pre-date the first pregnancy at 14-<18 yo.b. Occupation of the woman in her early 20’s could be an interesting “outcome” variable, understanding that the association is complex and may be two-way.7. Methods: Line 159 and onward – please provide the actual numbers in parentheses with the percentages. This helps the reader track the denominators of the different percentages.8. Table 1: suggest changing “1st birth <18, Repeat” to “1st birth <18, Repeat birth <20” for clarity9. Table 2: I'm not sure this table adds anything. It is quite busy and a bit confusing. The bottom line is that there is a pretty consistent downward trend in repeat adolescent birth over the time period, but it is not as consistent if you look at just the percentage of women with adolescent birth who go on to have a second adolescent birth - this is an important potential area for intervention. I think this could be more compellingly demonstrated using a graph.10. Table 3: Again, this is complicated to present. I'm sure number of births was not normally distributed, so numerically presenting mean and SD is not particularly meaningful. Again, graphing means might give a nice visual.11. Table 5: bottom rows - I think this is not necessary to include... or could go in the supplement. It is obvious that women at the older end of the age range are less likely to have had their children as adolescents within the previous 5 years.12. Table 6: (and line 267-269) Urban/Rural residence and region both look highly significant in the univariate analysis. Is there a problem with collinearity between these variables that leads to loss of significance in the multivariable analysis? What happens if you include only one  in the analysis – do you retain the effect?13. Discussion: Most of the statistics look the worst during the 2000 and 2006 surveys. Are there any policy or cultural pressures during this time (or before or after) that could explain these changes? It would be interesting to have some context here.14. Line 257: you talk about the fact that some girls desire this second adolescent pregnancy, but this data shows a large proportion do want to delay. This is an important advocacy point that should be highlighted (and gets a little lost).15. Line 280-282: Good point. It would be nice to mention breastfeeding rates in Uganda.16. Line 302-303: abortion (and to a lesser extent the more rare outcome of stillbirth) is an interesting limitation. A comment about whether or not termination of pregnancy (as opposed to spontaneous abortion) is available/legal in Uganda might give some helpful background.17. Line 319-20: “There is need to understand how poverty leads to repeat adolescent births…” Suggest adding “and vise versa.”18. Line 322-323: “Early child birth is a trap for many births, and this hasn’t change over time- this needs to be studied more.” This final sentence is a bit abrasive and negative. Other than further study, it doesn’t suggest any action to redeem this problematic situation. I would suggest: "Pregnancy and child birth increase contact with the health care system, and each contact should be seen as an opportunity for education and services to prevent subsequent unwanted pregnancies."Reviewer #2: 1. Review summary• This reviewed paper aims to understand trends and factors of repeated births among adolescents in Uganda, where adolescent childbirths are prevalent. The authors’ paper provides a solid contribution to a scant evidence base on repeated adolescent pregnancies in sub-Saharan Africa. As the authors acknowledge, while much has been studied about early childbearing among adolescents, less is known about adolescent childbearing frequency and timing following an initial childbirth.• For the most part, the arguments framed in the introduction are clear and support the rationale for the methods and analyses undertaken. The claims made in the conclusion are also mostly supported by the data presented in the results. There are however, a number of revisions that could be made to the analysis and framing of the paper, further described below.2. Overall suggestions for improvements• There are important revisions that could significantly improve the quality and rigor of the analysis. Broadly these include:o Focusing the analysis and results on descriptive trends and risk factors of repeated births and pregnancy spacing only . The inclusion of all three outcomes can be confusing and distracting to the reader, and takes away from the overall message and research question focused on childbirth frequency.o If all three trends analyses are kept, then a clearer connection should be made in the introduction justifying why all three outcomes are being used in the analysis (particular in using ‘pregnancy wantedness’ to show that women often don’t want their second pregnancy).o Additional and/or clearer information should be provided in the introduction, analysis and discussion about typical birth spacing practices within adolescent and adult populations, and how these spacing practices might affect childbirth repetition among adolescents. For example, it is unclear from this analysis whether repeat pregnancy is in fact a problem in this adolescent population, or whether repeat pregnancy among adolescents is simply reflective of typical birth spacing practices . It appears that the authors attempt to address this by providing additional analyses on birth spacing intervals in the results, but the connection and implications is still unclear for the reader.o Since this is a trends analysis, the authors should consider including additional risk factor models examining how the different risk factors change (or do not) across different DHS survey rounds (see more specific suggestion below).3. Suggestions for improvements by section3.1. Introduction:• The introduction could be strengthened by including additional information about current birth interval spacing in youth versus those of adult populations, in addition to current literature around pregnancy desires/wantedness • The consequences of repeated births are very well explained in the introduction, but no background information is provided on the risk factors of repeated pregnancies. This information should be included in the introduction to justify your variables selection in the risk factor analysis later on.3.2. Methods and analysis:• The rationale for using the DHS 2016 survey round for the risk factor analysis should be explicitly stated (rather than other rounds)• The risk factor analysis is interesting – if the data are available, you could expand on this analysis by using repeated cross-sectional surveys to assess changing risk factors over time to see how or if predictors have changed over time• Either in the introduction or methods, you should provide a justification for your choice of variables in the final risk factor model• Please elaborate what your concerns are about reverse causality (1-2 sentences suffice).• You may want to explain why other variables widely available in DHS were not included . You could also include this in your ‘Limitations’ section.• The use of the prevalence ratio makes sense and is appropriate. However, the rationale behind using poisson regression is unclear .3.3. Results• The contents of Table 1 should be explained prior to the sharing of table results in the text. The text and tables can be confusing to read. For example, in Table 1, it should be clarified that the data in the last row “among women 20-24 years with first birth <18 years” is a sub-set sample of those with a first birth before 18 years (the two rows directly above). This took me a bit to understand and sort out without any explanatory text.• Along with the above point, a clearer explanation of the contents in Table 2 will help clarify any confusions to the reader. For example, it was unclear what the intervals were for ‘5 years’ versus ‘first 15 years’ versus ‘last 15 years’ versus ’30 years’ in Table 2. Alternatively, you could explore presenting the data in Table 2 in a graph form.• You could potentially discard the information in Table 3 and in the text related to the mean/median number of births – this does not seem to provide much more valuable information and is not mentioned again in your paper later on.• The birth interval table (Table 4) could be strengthened by including data for first to second childbirth intervals among women 20-24 years with a first birth at or after 18 years. This could help the reader understand whether these birth intervals are typical across women who start childbearing both before 18 years and across women who start child birth at and after 18 years .3.4. Discussion:• If you keep the ‘ pregnancy wantedness’ analyses, please better elaborate in your discussion how these data help validate your findings on repeated pregnancies among adolescents.• On lines 238-239, you suggest that a defined measurement might be needed. What definition might you recommend based on the measures you used and analysis you conducted?3.5. Smaller edits:• In general, the language is clear. However, there are a number of grammatical errors which should be reviewed and corrected (for example ‘ration’ instead of ‘ratio’)• The use of ‘wantedness’ can be confusing, perhaps ‘pregnancy desire’ could be used instead (if appropriate)• Lines 68-72 – the logic of these sentences is not clear• Line 58 – Recommend including the data for the adolescent fertility rate here (in addition to the childbirth rates you have now)• Line 105 – I believe you mean “among women 20-24 years old with a first live birth”• Line 132-133 – Please explain what type of bias this might induce**********what does this mean?). If published, this will include your full peer review and any attached files.6. PLOS authors have the option to publish the peer review history of their article  digital diagnostic tool,  10 Mar 202009th/03/2020To Dr. Chaisiri Angkurawaranon Academic EditorPLOS ONEDear Dr. Angkurawaranon,Re; Response to reviewers’ comments and resubmission of revised manuscript ID PONE-D-19-31884Time Trends in and factors associated with Repeat Adolescent Birth in Uganda: analysis of six Demographic and Health SurveysThank you for reviewing and providing feedback on this manuscript. Please receive the revised copy with specific responses and changes summarized in the table below. Reviewers comment Response to comment Line numberJournal Requirements:When submitting your revision, we need you to address these additional requirements:1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming.  Thank you for this guidance. We have updated the entire manuscript and we believe it meets the journal requirements NA5. Review Comments to the AuthorReviewer One:This manuscript quantitatively describes repeat adolescent pregnancies in Uganda over almost 30 years. This is a valuable analysis of a meaningful outcome in a vulnerable population, and certainly deserves publication. Please see comments below. Overall, the data (and other previous data) is looked at from many angles and it is important to clarify and highlight the differences in the different statistics offered – eg what is the denominator? is it a rate or a prevalence? etc. I applaud the authors for tackling this complicated subject and look forward to seeing the final publication. Thank you so much for this feedback and comments. We have proceeded to clarify on the different statistics offered, in the details below.Statistics offered for trends are prevalence and the different denominators are clarified in the methods section for each sub-analysis. Pages 5-8Major Comments: ONE1. The authors discuss the lack of a standardized definition of repeat adolescent pregnancy as a challenge. However, they chose not to use the definitions previously used in the literature. Please explain the rationale for your definition and explain why the benefit of doing the analysis in this way was big enough to outweigh the drawback of lack of comparability with other reports.  Thank you so much for this observation. Previous studies examined getting/reporting a repeat adolescent pregnancy as an end point and did not examine pregnancy outcome. This is a challenge as some pregnancies get aborted or end in stillbirths. An outcome of a repeat live birth is of major importance as it leaves the adolescent mother with another offspring to care for.Younger adolescents, <18 years, face more disadvantages than older adolescents. This group is more vulnerable and a second or higher birth among this category of adolescents, while still in adolescence, may compound the disadvantages for the woman and offspring.We based repeat birth on live births (rather than pregnancies) because that is what is available from the DHS over the many surveys. Lines 118-120, page 6Lines 283-287, page 15Major Comments: TWO 2. A major advocacy point that emerges from this data is the need for every contact between healthcare workers and pregnant adolescents to be seen as an opportunity to prevent unwanted repeat adolescent birth. This is powerful and actionable. It is amazing that the overall rate of adolescent birth is falling over time, but the rate of repeat adolescent birth (arguably easier to target) is not. Thank you for this very important advocacy point. We have emphasized it in the abstract and conclusion section. Line 48-50, page 3Line 377-380,Pages 19 & 20Minor Comments:1. There are some minor areas of awkward wording in English. Eg:a. Line 42 and 48: “More women with repeat adolescent births preferred to have…” consider "Many women with repeat adolescent births..." or "An increasing number of ...” Thank you for noting this. We have made the correction. Line 42, page 2Line 48, page 3b. Line 68-69: “yet, the way the sexual intercourse came about may be linked to sexual and gender based violence…” consider: “and many of these pregnancies are result of sexual and gender based violence" Thank you. This has been corrected. Line 68-69, page 4c. Line 318-19 “This qualitative research should understand this” consider: “This qualitative research should investigate this” or “This qualitative research should approach this” This adjustment has been made. Line 381 & 382, page 202. Line 57-60 wording is unclear. First half of sentence says no change, the second half says there has been a decline. Consider: “These levels have remained high in the last 15 years despite a decline in the age specific fertility rate for Uganda among this category of women, 195 births per 1,000 women in the 1990 Uganda Demographic and Health Survey (UDHS) to 132 births per 1,000 women at the 2016 survey.” Also, I think these numbers should be “per 1,000 women per year”. Thank you so much. This has been corrected. Line 57-60, page 33. Line 79: There are other studies that look at adolescent births and then estimate repeat pregnancies (though not the primary outcome) eg: Parker AL, Parker DM, Zan BN, Min AM, Gilder ME, Ringringulu M, et al. Trends and birth outcomes in adolescent refugees and migrants on the Thailand-Myanmar border, 1986-2016: an observational study. Wellcome Open Research. 2018 May 21;3:62. Thank you so much for this literature. We have taken this into consideration and cited it. Lines 83-85, page 44. Line 96: How were surveys conducted? How many languages? In person facilitation? This gives an idea of what into the data collection. Could use a reference if it is described elsewhere. Thank you for noting this. This information has been added Line 108-111,Page 5 & 65. Line 123-125: The decisions on these categories seem a little arbitrary but I don't know the religious dynamics in Uganda intimately. I would expect Baptist might be appropriate to group with Anglican and Penticostal under "Protestant"... Alternately, Anglican and Penticostal are quite different in some parts of the world (but maybe not in Uganda). This may be fine as is but it struck me as a bit odd. The “traditional” religion group seems like a potentially interesting cohort but it may have been very small numbers and therefore hard to look at. Thank you for this question. We did this based on the Uganda dynamics for which the minority group were placed under “other”. The Anglican and protestant are similar and are second to Catholics in number. Third is Muslims. We discussed this decision among the 4 Ugandan authors on this study, two of whom are social scientists and provided detailed feedback on the appropriateness of this categorization. NA6. Line 127-129: these variables that were excluded due to potential reverse causality seem very interesting.a. Educational level at a certain point predates the pregnancy – for example, “no education” or “some primary school” or “completed primary school” could be interesting variables that would be expected to pre-date the first pregnancy at 14-<18 yo.b. Occupation of the woman in her early 20’s could be an interesting “outcome” variable, understanding that the association is complex and may be two-way. This is a very important observation. Indeed we discussed this issue within the co-author team, and feel we made the best decision given the limitations of cross-sectional survey data. Education level and occupation can be both predictors and outcomes of first birth; we cannot examine such complex relationships from these datasets, unfortunately. Given that we wanted to produce rigorous results and the fact that at the point of survey-when the women were 20-24 years, we thought it would not be rigorous to include them as predictors of repeat adolescent birth , due to potential reverse causality. We agree with the reviewer that it would be great to look at them at the point of second birth- prospectively, in future studies. NA7. Methods: Line 159 and onward – please provide the actual numbers in parentheses with the percentages. This helps the reader track the denominators of the different percentages. Thank you so much. We have included the actual numbers. Lines 182-194, page 98. Table 1: suggest changing “1st birth <18, Repeat” to “1st birth <18, Repeat birth <20” for clarity We have made this clarification. Table 1, page 10Line 1959. Table 2: I'm not sure this table adds anything. It is quite busy and a bit confusing. The bottom line is that there is a pretty consistent downward trend in repeat adolescent birth over the time period, but it is not as consistent if you look at just the percentage of women with adolescent birth who go on to have a second adolescent birth - this is an important potential area for intervention. I think this could be more compellingly demonstrated using a graph. Thank you so much for this guidance. This table summarizes the percent point differences between surveys, the initial 15 years, last 15 years and overall difference. We feel it would be essential for the reader to easily access this information and it has been moved to a supplementary material- S1 Table, attached. Further, we have provided a graph for repeat adolescent birth following first birth <18 years and maintained details in table 1, last row. Lines 187 & 188, Page 9 S1 Table- attachment10. Table 3: Again, this is complicated to present. I'm sure number of births was not normally distributed, so numerically presenting mean and SD is not particularly meaningful. Again, graphing means might give a nice visual. We thank the reviewer for this important point. While we agree that a visual might be more aesthetically pleasing, we believe that some of our readers are interested in the precise estimates and confidence intervals , which is why we would strongly prefer to keep this table as is. In terms on the distribution, we present number of births before age 20 among women who are 20-24 years old, so there is not a range of values of parity, like there might be among a population of women in reproductive age. We share the reviewer’s concern about how this distribution is presented, and we therefore show several statistics, including mean, median (among various populations by adolescent fertility pattern), and % with 3+ births. We hope that this captures the breadth of descriptives on this population. NA11. Table 5: bottom rows - I think this is not necessary to include... or could go in the supplement. It is obvious that women at the older end of the age range are less likely to have had their children as adolescents within the previous 5 years. This guidance is noted. We have deleted those rows and this table is now table 4. Line 228- Table 4,page 1212. Table 6: (and line 267-269) Urban/Rural residence and region both look highly significant in the univariate analysis. Is there a problem with collinearity between these variables that leads to loss of significance in the multivariable analysis? What happens if you include only one  in the analysis – do you retain the effect? Thank you for noting this. There is no collinearity as each of the five regions has both urban and rural clusters. The variable responsible for the loss of effect is “age at first birth”. NA13. Discussion: Most of the statistics look the worst during the 2000 and 2006 surveys. Are there any policy or cultural pressures during this time (or before or after) that could explain these changes? It would be interesting to have some context here. During this period is when Uganda rollout the implementation of universal primary education and the defilement act (sex or marriage with a girl <18 years was prohibited under the law)- from 1997. The effects of these policies would perhaps have started to be felt after the 2000 survey-in the younger adolescent age groups that had time to benefit from it. Therefore, based on the sample analyzed (women 20-24 years), the cohort that would have benefited from these interventions would have been the one surveyed in 2011. We have added this context in the discussion section. Lines 300-306, page 1614. Line 257: you talk about the fact that some girls desire this second adolescent pregnancy, but this data shows a large proportion do want to delay. This is an important advocacy point that should be highlighted (and gets a little lost). Thank you for bringing this important point to our attention. We have highlighted this advocacy point in the discussion section. Line 341-346, page 1815. Line 280-282: Good point. It would be nice to mention breastfeeding rates in Uganda. Thank you. We have added information on breastfeeding. Line 333 & 334, page 1816. Line 302-303: abortion (and to a lesser extent the more rare outcome of stillbirth) is an interesting limitation. A comment about whether or not termination of pregnancy (as opposed to spontaneous abortion) is available/legal in Uganda might give some helpful background. Thank you. We have added this information. Line 361-364, page 19 17. Line 319-20: “There is need to understand how poverty leads to repeat adolescent births…” Suggest adding “and vise versa.” We have done this. Thank you Line 383, page 2018. Line 322-323: “Early child birth is a trap for many births, and this hasn’t change over time- this needs to be studied more.” This final sentence is a bit abrasive and negative. Other than further study, it doesn’t suggest any action to redeem this problematic situation. I would suggest: "Pregnancy and child birth increase contact with the health care system, and each contact should be seen as an opportunity for education and services to prevent subsequent unwanted pregnancies." Thank you so much for this guidance. We have corrected this. Lines 377-380, page 19 & 20Reviewer #2:1. Review summary • This reviewed paper aims to understand trends and factors of repeated births among adolescents in Uganda, where adolescent childbirths are prevalent. The authors’ paper provides a solid contribution to a scant evidence base on repeated adolescent pregnancies in sub-Saharan Africa. As the authors acknowledge, while much has been studied about early childbearing among adolescents, less is known about adolescent childbearing frequency and timing following an initial childbirth.• For the most part, the arguments framed in the introduction are clear and support the rationale for the methods and analyses undertaken. The claims made in the conclusion are also mostly supported by the data presented in the results. There are however, a number of revisions that could be made to the analysis and framing of the paper, further described below. Thank you very much for this feedback and all the suggestions for improvement. We have addressed the revisions as below. NA2. Overall suggestions for improvements• There are important revisions that could significantly improve the quality and rigor of the analysis. Broadly these include:o Focusing the analysis and results on descriptive trends and risk factors of repeated births and pregnancy spacing only . The inclusion of all three outcomes can be confusing and distracting to the reader, and takes away from the overall message and research question focused on childbirth frequency. Thank you so much for this important observation and guidance. We analyzed wanteness as part and parcel of birth spacing because it can explain the spacing information obtained. Further, it also speaks to the trends observed. We therefore feel, this information adds to advocacy and therefore programming for this group of women.However, as highlighted by the reviewer, our most important findings relate to the prevalence of repeat births over time and the predictors, which are the crucial points in our Discussion section. NAo If all three trends analyses are kept, then a clearer connection should be made in the introduction justifying why all three outcomes are being used in the analysis (particular in using ‘pregnancy wantedness’ to show that women often don’t want their second pregnancy). Thank you so much for this. We analyzed wantedness as a descriptor. We have provided more clarification in the introduction on its relatedness to the trends and birth intervals Lines 86-89, page 4 & 5.o Additional and/or clearer information should be provided in the introduction, analysis and discussion about typical birth spacing practices within adolescent and adult populations, and how these spacing practices might affect childbirth repetition among adolescents. For example, it is unclear from this analysis whether repeat pregnancy is in fact a problem in this adolescent population, or whether repeat pregnancy among adolescents is simply reflective of typical birth spacing practices . It appears that the authors attempt to address this by providing additional analyses on birth spacing intervals in the results, but the connection and implications is still unclear for the reader. Thank you for this helpful feedback. We have provided additional information on birth spacing among adolescents compared to adult women - in the Introduction and Discussion sections. Lines 89-92, page 4 & 5.Lines 337-340, page 18o Since this is a trends analysis, the authors should consider including additional risk factor models examining how the different risk factors change (or do not) across different DHS survey rounds (see more specific suggestion below). This is a very important recommendation and we are considering this for another paper. We have discussed this in the co-author team and feel that risk factor analyses across surveys do not fit within the objective and scope of this paper. NA3. Suggestions for improvements by section3.1. Introduction:• The introduction could be strengthened by including additional information about current birth interval spacing in youth versus those of adult populations, in addition to current literature around pregnancy desires/wantedness  Thank you. We have done this Lines 86-92, page 4 and 5.• The consequences of repeated births are very well explained in the introduction, but no background information is provided on the risk factors of repeated pregnancies. This information should be included in the introduction to justify your variables selection in the risk factor analysis later on. We have added this information. Line 72-75, page 43.2. Methods and analysis:• The rationale for using the DHS 2016 survey round for the risk factor analysis should be explicitly stated (rather than other rounds) We chose the most recent survey, 2016, for this analysis to tease out the most current risk factors for intervention purposes and to inform policy-makers in Uganda . We have added this rationale. Line 128-130, page 6• The risk factor analysis is interesting – if the data are available, you could expand on this analysis by using repeated cross-sectional surveys to assess changing risk factors over time to see how or if predictors have changed over time Yes, these data are available. We do request that this be analyzed for another paper. NA• Either in the introduction or methods, you should provide a justification for your choice of variables in the final risk factor model We chose variables based on previous literature and on what was available in the DHS data. We have added this justification. Line 146-147, page 7• Please elaborate what your concerns are about reverse causality (1-2 sentences suffice). Thank you. This has been done. Lines 144-146, page 7• You may want to explain why other variables widely available in DHS were not included . You could also include this in your ‘Limitations’ section. We have added this explanation in the methods section and limitations section. Briefly, the DHS measure contraceptive use at the time of survey, so this happens after the first and repeat adolescent childbirth and can therefore not be a risk factor. Lines 147-149, page 7Lines 363-364, page 19• The use of the prevalence ratio makes sense and is appropriate. However, the rationale behind using poisson regression is unclear . Thank you so much for this observation. In scenarios where the likelihood of the outcome is high, above 10%, the modified Poisson regression if preferred to the logistic regression to avoid odds ratios overestimating the prevalence ratios. We did provide this explanation and a reference. Line 162-165, page 83.3. Results• The contents of Table 1 should be explained prior to the sharing of table results in the text. The text and tables can be confusing to read. For example, in Table 1, it should be clarified that the data in the last row “among women 20-24 years with first birth <18 years” is a sub-set sample of those with a first birth before 18 years (the two rows directly above). This took me a bit to understand and sort out without any explanatory text. Thank you. This explanation has been provided in text and we have also provided additional detail in the row headings. Lines 180-181, page 9Table 1• Along with the above point, a clearer explanation of the contents in Table 2 will help clarify any confusions to the reader. For example, it was unclear what the intervals were for ‘5 years’ versus ‘first 15 years’ versus ‘last 15 years’ versus ’30 years’ in Table 2. Alternatively, you could explore presenting the data in Table 2 in a graph form. Thank you so much for this guidance, we have opted to have this as a supplementary table. This will enable readers easily access the percent point differences between surveys NA• You could potentially discard the information in Table 3 and in the text related to the mean/median number of births – this does not seem to provide much more valuable information and is not mentioned again in your paper later on. Thank you for this suggestion. We feel this provides valuable information on the distribution of the numbers of births, by adolescent fertility pattern. We would be willing to consider placing this table into Supplementary material given editorial guidance. At the moment, we added a few sentences to the text referring to this finding in more detail. NA• The birth interval table (Table 4) could be strengthened by including data for first to second childbirth intervals among women 20-24 years with a first birth at or after 18 years. This could help the reader understand whether these birth intervals are typical across women who start childbearing both before 18 years and across women who start child birth at and after 18 years . Thank you for this very important observation. We however wanted to restrict our analysis to the trend in birth intervals among this category of women in order to assess if there have been any changes within this category. Expanding it as suggested is a very important recommendation and we have included this in the suggestions for future analysis as it is outside the scope of this paper. Lines 385-389, page 203.4. Discussion:• If you keep the ‘ pregnancy wantedness’ analyses, please better elaborate in your discussion how these data help validate your findings on repeated pregnancies among adolescents. Thank you. We have elaborated this in the discussion section. Line 341-346, page 18• On lines 238-239, you suggest that a defined measurement might be needed. What definition might you recommend based on the measures you used and analysis you conducted? We recommend a definition that looks at those initiating birth <18 years, the most vulnerable group in early adolescence. We have included this information. Line 283-287, page 153.5. Smaller edits:• In general, the language is clear. However, there are a number of grammatical errors which should be reviewed and corrected (for example ‘ration’ instead of ‘ratio’) Thank you so much for this observation. We have corrected the grammatical errors. Lines 244 and 253, page 13• The use of ‘wantedness’ can be confusing, perhaps ‘pregnancy desire’ could be used instead (if appropriate) This word has been used by other publications using DHS data and the DHS also looks at wanting then, later or not all. We opted to retain it as wantedness.Pregnancy desire might be erroneously interpreted as measured while the woman was pregnant; while on the DHS this is a retrospective question asking women to recall pregnancy wantedness up to 5 years after they had a live birth. NA• Lines 68-72 – the logic of these sentences is not clear These sentences position our age category, having births <18 years, and the fact that they are the most vulnerable to sexual abuse. NA• Line 58 – Recommend including the data for the adolescent fertility rate here (in addition to the childbirth rates you have now) These were the adolescent fertility rates (15-19 years). We have corrected this. Line 58, page 3• Line 105 – I believe you mean “among women 20-24 years old with a first live birth” Yes, we have corrected this Line 117, page 6• Line 132-133 – Please explain what type of bias this might induce We have done so Line 153, page 7AttachmentResponse to Reviewers.docxSubmitted filename: Click here for additional data file. 26 Mar 2020Time Trends in and factors associated with Repeat Adolescent Birth in Uganda: analysis of six Demographic and Health SurveysPONE-D-19-31884R1Dear Dr. Amongin,We are pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it complies with all outstanding technical requirements.Within one week, you will receive an e-mail containing information on the amendments required prior to publication. 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Preterm birth is a common cause of death worldwide of children under the age of five years. This condition is linked with short and long term neonatal morbidity and mortality. Maternal nutrition during pregnancy has a profound effect on fetal growth and development and subsequently also on the incidence of preterm birth. The aim of this study was to assess the differential effect of dietary patterns of pregnant women across ordered levels of preterm birth. Dietary assessments were performed using a food frequency questionnaire, presented to 687 pregnant women, in the “Mother and Child in the Environment” birth cohort during the period of 2013 to 2017. Each pregnancy resulted in a live birth. Eight dietary patterns were extracted, using exploratory factor analysis. The partial proportional odds model was employed to model severity levels of preterm birth. The partial proportional odds model has been recognized to be a flexible approach since it allows the effect of predictor variables to vary across categories of the ordinal response variable of interest. Women with increased consumption of vegetable-rich foods showed a reduced risk of very to moderately preterm birth incidence , p = 0.036). Lower odds of very/moderately preterm birth compared to late preterm or term birth were observed for women following “nuts and rice foods” dietary pattern , p = 0.003). High dietary consumption of starch foods dietary pattern , p = 0.014) was associated with the most severe level of preterm birth outcome incidence, i.e. very/moderately preterm birth. The partial proportional odds modeling allowed the description of the effect of maternal dietary patterns across the different severity levels of preterm birth. Preterm birth is associated with short and long term neonatal morbidity and mortality5 and is the second-leading cause of under-five mortality, worldwide1. The global prevalence of preterm birth is 10%, accounting for 15 million births, globally and approximately 50% of all perinatal deaths every year6. In South Africa, more than 8 out of 100 babies are preterm. The country was ranked 24th out of 184 countries in 2010, for the number of newborn deaths, due to complications from preterm birth1.The World Health Organization (WHO) has defined preterm birth as the spontaneous or induced live delivery of babies before 37 completed weeks of gestation5. Increased maternal age during pregnancy, infertility treatment and maternal health conditions are the leading causes (1). However, changes in obstetric practice , as well as the late preterm category (34–36 weeks’ gestation)8. Although considerable attention has been paid to preterm birth as a whole, outcomes vary with the subcategories of preterm birth11. The rate of preterm birth has increased by 33% in the last 25 years, almost entirely due to the rise in late preterm births12. The extent of the increased risk of preterm birth associated with socio-economic disadvantage and other risk factors generally increased with an increasing severity level of preterm birth11.With a few exceptions, the rate at which preterm birth occurs has grown in both developed and developing countries, over the last decade16 and preterm deliveries21. Several studies have yielded varying results concerning the associations between single foods or nutrients during pregnancy and preterm birth incidence26. Other studies looking at dietary patterns of foods in combination during pregnancy have allowed for the interactive effect between nutrients and timing of deliveries to be studied28. As an example, a western diet, which is described as consisting of a high consumption of fried and processed meats, is associated with an increased risk of preterm birth29, while prudent diets, which are rich in vegetables and fruits, have been associated with a lower occurrence of preterm birth23.Maternal nutrition has an effect on fetal growth31. Many researchers have applied newer statistical and scientific methods to identify the effect of dietary patterns on the incidence of preterm birth. Since it is not appropriate to assume that all the factors have the same effect across different severity levels of preterm birth, an ordinal logistic regression model was proposed for such investigation by Walker and Duncan32 and was later referred to as the proportional odds model36. The proportional odds model is a generalization of a binary logistic regression model, in which the response variable has more than two ordinal categories. The use of ordinal polytomous responses has increased significantly in health science studies on quality of life, defining health status indicators, the severity of certain diseases and the effectiveness of post-operative procedures36. Data in such studies are commonly evaluated by the proportional odds model37. The partial proportional odds model combines the ordered arrangement in ordinal models, while allowing for particular independent variables to affect different levels of the outcome variables28.The complexity of preterm birth suggests that a simple binary logistic regression approach fails to identify exposure effects at different severity levels of preterm birth38. It is, therefore, useful to assess the association of dietary patterns during pregnancy with preterm birth, in order to make informed decisions on dietary interventions during pregnancy27. We used a novel modeling strategy, the partial proportional odds model, which allows for more nuanced insights on the effect of dietary patterns across different severity levels of preterm birth than other approaches, such as the binary and multinomial logistic regression models. This paper attempts to model the differential effect of maternal dietary patterns, across the severity levels of preterm birth using the partial proportional odds model.Even though maternal diet has been considered as a determinant of preterm birth prevalence in previous studies, the association with preterm birth is not well established in South Africa. South Africa is experiencing a transition in dietary patterns, from that of traditional diets to a more “Western” diet, high in fats and sugarThe Mother and Child in the Environment (MACE) birth cohort is based among low-income communities in Durban, South Africa. The study enrolled 996 pregnant women between March 2013 and May 2017. Participants in the study were selected from public sector antenatal clinics in the industry dense residential areas in south Durban , as well as from residential located areas in the less heavily industrial areas in the north of Durban . All pregnant women that met inclusion and exclusion criteria, were enrolled in the study and followed up during their pregnancy, through to labour and delivery. The inclusion criteria included gestational age less than 20 weeks and resident for the full duration of the pregnancy in the geographical area within which the clinic was located. Women with multiple pregnancies were excluded. Ethical approval was obtained from the University of KwaZulu-Natal’s Biomedical Research Ethics Committee, and each participant provided informed consent, participation was voluntary and withdrawl from the study at any point was allowed.1 and Donoghue et al.39, preterm birth is classified as very preterm (<32 weeks), moderately preterm (32–33 weeks), late preterm (34–37 weeks), and term (38–42 weeks).Among the 996 enrolled pregnant women, 309 subjects were excluded due to miscarriages, termination of pregnancy and loss to follow up. The study analysed data on 687 pregnant women in the cohort. The food frequency questionnaire was administered to the mothers in the third trimester. Dietary patterns were based on the 75 food items commonly used in the maternal diet. According to WHOTo reduce the 75 dietary variables into a set of manageable latent characteristics, with minimal loss of information, exploratory factor analyses with a Promax rotation method was used. A scree plot Fig. , along wExploration of the data was performed using parallel coordinate plots (PCP), in order to examine trends of dietary patterns across different severity levels of preterm birth. The PCP revealed an association between the higher-order severity levels 2 and 3 of preterm birth outcomes, with lower consumption of ‘nuts and rice foods’ dietary patterns , i = 1… n, is defined as followsGestational age was categorized into three levels, based on the severity levels of preterm birth. The outcome variable of interest (severity level of preterm birth) was measured on three ordinal levels of term (38–42 weeks), late preterm (34–37 weeks) and very (<32 weeks) or moderately preterm (32–33 weeks) birth. These sub-categories of preterm birth were coded to represent severity levels: term birth = 1, late preterm birth = 2, very or moderately preterm birth (most severe) = 3. The level k = 1 defines the lowest severity level (term birth). Therefore, for the iβ, across severity levels of preterm birth, while maintaining its ordinal nature. It estimates the cumulative probabilities of being at or below, specific severity levels of preterm birth, given a set of independent variables40. The partial proportional odds model can be given asx is the vector of observed explanatory variables: maternal and infant characteristics, including maternal dietary patterns, maternal age, maternal education, employment, maternal annual income, family size, gravida, and infant gender, β is the vector of estimable parameters and α is the unknown threshold, or intercept parameters. Consequently, the model in Eq. A proportional odds model is one in which the response variable has more than two ordinal categories, with the assumption of odds being the same across the categories. The partial proportional odds model is suitable in modeling severity levels of preterm birth due to the flexibility in the procedure, as it is capable of relaxing the proportional odds assumption in the ordered logit model, by allowing the variability of the regression parameter 41. The predictors that met the proportional odds assumption, dietary patterns  and covariates , have the same coefficient values in two of the cumulative logits, the higher severity levels (levels 2 or 3) versus the lowest level (level 1) and the most severe level (level 3) versus the lower severity levels (levels 1 or 2). We considered p-values below 0.05 to be statistically significant. SAS 9.4 was used for the analysis of the data.The proportional odds, in particular, the probability of an observation being at, or above, a specific severity level of preterm birth, was conditional on maternal dietary practices and socio-demographic characteristics observed in the study. In fitting a partial proportional odds model, it is imperative to verify the homogeneity of the proportional odds ratios across all ordinal levels, using a global test of non-proportionalityWritten, informed consent was obtained from the mother for the children and provided their own consent. The study was approved by the Biomedical Research Ethics Committee of the University of KwaZulu-Natal.The proportion of preterm birth in the MACE cohort was 17%, with 14% late preterm birth and about 3% very to moderately preterm births. As for the profile of the mothers, the majority were between 20 and 29 years of age (60.4%), multigravida (57.1%), high school graduates (79.4%), unemployed (81.5%) and had no personal income (45.3%). Male babies predominated (52.1%).The Score test indicated that the proportional odds assumption is not reasonable, suggesting that separate parameters are needed across the cumulative logits for at least one predictor (p = 0.006). Thus, we conducted model comparison among the three ordinal logistic regression models, with likelihood ratio tests. The proportional odds model was rejected in favor of both the non-proportional odds model (p = 0.001) and the partial proportional odds model (p = 0.0001), while the partial proportional odds model fits as well as the non-proportional odds model (p = 0.305). The Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) values were used to compare the performance of the non-proportional and partial proportional odds models. The partial proportional odds model had the lowest AIC and BIC values, compared to the non-proportional odds model Table . This suThe proportional odds assumption was violated for ‘starch foods’, ‘nuts and rice foods’ dietary patterns and maternal income, family size and gravida, among socio-demographic factors. The dietary patterns ‘energy foods and snacks’, ‘spreads and fast foods’, ‘butter, junk foods, and juices’ and ‘protein-rich foods’, ‘vegetable-rich foods’, ‘alcoholic drinks’ and the adjusted socio-demographic variables maternal age, education, employment, and infant gender, all satisfied the proportional odds assumption.2 (36) = 57.42, p < 0.0001, indicating that the full model with predictors, provided a better fit than the null model, with parameters significantly being non zero , p = 0.036)  dietary pattern, was associated with a lower risk of preterm birth. However, our study showed that women who follow ‘starch foods’ dietary pattern , is associated with the most severe level of preterm birth. Here, the effect of ‘starch foods’ dietary pattern, varied across the different severity levels of preterm birth. This is due to the use of the partial proportional odds model, which considered the severity level of preterm birth, on top of other studies that use the multinomial logistic regression model. This may also be attributed to the diets having a high glycemic index, such as potatoes and potatoes fried with fat. These have a high loading on the starch foods dietary pattern by increasing blood glucose levels, similar to the study focusing on pregnant women with diabetes, who deliver preterm babies47. An increase in consumption of alcoholic drinks was associated with a higher risk of preterm birth, this association being similar across all severity levels of preterm births. Moreover, the study indicated that older age mothers had a significantly higher risk of preterm birth across all severity levels. This is consistent with other studies that showed that the risk of having a preterm baby was higher for older mothers, and further that this risk increased, with an increase in severity level of preterm birth48.A Norwegian mother and child cohort studyThe strength of this study lies in the use of a more parsimonious method, the partial proportional odds model, that allows the predictors that meet the proportional odds assumption to take the same coefficient for all severity levels of preterm birth, and other predictors to vary across the severity levels of preterm birth, thereby ensuring that there is no potential loss in accuracy of prediction.41. As a result, we used the partial proportional odds model, which accommodates the variables that failed to satisfy the proportional odds model assumption. Unlike previous studies, which were based on the effect of consumption of single food items, a data-driven exploratory factor analysis was used to extract dietary patterns that facilitate understanding of a variety of food consumption habits of the cohort. The limitation of the study is that the data is only adjusted for socio-demographic factors. It is possible that our findings could be biased due to unmeasured confounding variables.Ordinal models assume the proportional odds assumption across severity levels of preterm births, compared to multinomial logit models, which completely ignore the sequential order of preterm births. The assumption of proportional odds model, however, was not supported by the data for all the factors. The partial proportional odds model bridges the gap between the proportional odds and the multinomial logit modelsThis study found that the ‘vegetable-rich foods’ and ‘nuts and rice foods’ dietary patterns were associated with a reduced likelihood of preterm birth. The dietary patterns such as ‘starch foods’ and ‘alcoholic drinks’ and older age were factors that were associated with an increased likelihood of preterm birth. The study further showed ‘starch foods’ and ‘nuts and rice foods’ dietary patterns, as well as family size, had a differential effect at different severity levels of preterm birth. The use of partial proportional odds model allowed for modeling the effect of dietary patterns on the severity of preterm birth, allowing for flexibility over the assumption of homogeneity of threshold-specific covariate effects and may consequently allow for a more proper application of models for ordered responses, than is the case for the standard proportional odds methods.


In sub-Saharan Africa, there is high coverage of the recommended four or more times antenatal care (ANC) visits during pregnancy without complications; notwithstanding this achievement, the negative birth outcomes related to childbirth such as low birth weights and stillbirths are still high despite the increased access to antenatal services. Hence, the study assessed the association between antenatal attendance and birth weight in the Wa East District. p value < 0.05 was considered a significant association between dependent and independent variables.  The cross-sectional study design was used with a semistructured questionnaire to collect data from mothers who delivered within a one-year period through a review of antenatal and birth records from health facilities where the women delivered and interviewed. The chi-squared test and univariate and multivariate logistic regression were performed to establish the association between normal birth weight and ANC services the woman received and other predictor variables, and p = 0.044], religion (Islam) , and educational level (SHS/tertiary)  were the background characteristics associated with normal birth weight (2.5-40 kg). Also, women who had their urine tested at the ANC clinics  and women who received a long-lasting insecticide-treated net  from the ANC clinic were associated with normal birth weight.  The study involved 233 women. About 62.2% attended ANC clinics 4+ times before giving birth, 70.0% did not received the minimum ANC services required for every pregnant woman, 0.9% of pregnancies resulted in stillbirth, and 24.5% of babies born had a birth weight < 2.5 kg. Women marital status  [AOR: 2.05, 95% CI: 1.33-6.89,  Notwithstanding the benefits of antenatal care services, only 62.2% of pregnant women attended 4 or more ANC visits before giving birth, while 70% did not received the services they need. These might have influence the 24.5% of babies born with a low birth weight. Therefore, there is a need for special attention from all stakeholders to reverse the trend. Antenatal care (ANC) is a fundamental component of routine maternal and child health services and provides opportunities for many different services to be offered to pregnant mothers with the aim of screening and detecting danger signs early and providing the necessary timely interventions. The goal of antenatal care is to reduce maternal and child mortality and morbidity; for example, the roll-out of antimalarial drugs and antiretroviral therapy for maternal HIV/AIDS are services offered at antenatal care sessions to reduce maternal and child morbidity and mortality . TherefoPregnant women stand a greater risk of losing their lives or their babies if proper care and attention are not given to them during antenatal care until delivery. Globally, in 2015, the maternal mortality ratio was estimated to be around 216 per 100,000 live births and occurring mostly in low-resource settings  due to pIn Ghana, the maternal mortality ratio is estimated at 310/100,000 live births, while neonatal and infant mortality rates were 25/1,000 and 37/1,000 pregnancies , respectThe Wa East District is a complete rural district with 145 communities. Currently, the district has an estimated population of about 87,182 with a 3.9% fertility rate. There are 9 health centres, a health post/clinic, and 28 functional community-based health planning and service (CHPS) facilities in the district rendering health services to the people. Although there are no hospitals and polyclinics in the district, the health centres there possess the needed resources and the potentials to render the basic emergency obstetrics and newborn care (EmONC) services to whoever needs these services .The study population includes women aged 15 to 45 years, who attended ANC clinics and delivered in health facilities in the district between June 2017 and May 2018. However, women who went through a caesarean section before delivery were excluded from the study because a study has shown that babies delivered through a caesarean section may gain more weight due to the fluids given to the mother before and during surgery . Also, wA descriptive cross-sectional design was employed to collect the study data at one point in time. The study involved reviewing individual records of women who gave birth at a health facility between June 2017 and May 2018 and interviewed the women for additional information. Variables that data were collected in the facility include parity, number of ANC visits made before delivery, birth weight of the child, still/live birth, and a minimum amount of ANC services received by mothers before delivery. Data collected from the mothers directly include the mothers' demographic data and counselling information given to mothers during ANC visits. Mothers' ANC booklets were referred to as where necessary to ascertain the needed information.n = z2 × p(q)/d2 , women who were Muslims , and women who have attained secondary/tertiary education . The associations indicated that married women were 2.7 times more likely to give birth to children with a normal birth weight than women who were in cohabitation relationships, women who were Muslims were 61% less likely to deliver babies with a normal birth weight than those who were Christians, while women with secondary/tertiary education were about 3.2 times more likely to give birth to children with a normal birth weight than women with no education.The chi-squared analysis showed significant associations between birth weight and marital status , urine test (p = 0.000), times sulphadoxine-pyrimethamine (SP) tablets received (p = 0.018), syphilis test (p = 0.003), LLITN distribution (p = 0.002), counsel on FP (p = 0.008), counsel on nutrition (p = 0.013), and birth outcome (p = 0.013).Again, chi-squared analysis showed significant associations between the birth weight and checking of hemoglobin levels  of the mothers in Wa East District never went to school. Meanwhile, decreasing levels of education has been linked to lower birth weight . Again, Globally, pregnant women are to make at least four or more antenatal care visits from skilled personnel before giving birth . HoweverApproximately 24.5% of babies born between June 2017 and May 2018 were below the normal birth weight (<2.5 kg). In contrary, the Nigerian studies and the Ethiopian study reported prevalence  of a low birth weight, which are lower than what the present study reported –31. MeanReligion was found to be associated with birth weight in the present study and agrees with a study in India , and in Mothers who had their urine tested were found to be associated with given birth to a child with a normal birth weight, and a study has shown that mothers who had their urinary metabolites measured at the end of the first trimester are associated with an increased risk of negative birth outcomes . Also, mNotwithstanding the benefits of antenatal care services, only 62.2% of pregnant women attended 4 or more ANC visits and 70% of the mothers did not receive all ANC services required. Also, 24.5% and 0.9% of pregnancies resulted in low birth weights and stillbirths, respectively. Normal birth weight is significantly associated with maternal religion, educational attainment, marital status, mothers who received long-lasting insecticide-treated nets, and those who had their urine tested. Inconsistency in logistics and medical supply to health facilities in the district should be addressed to ensure mothers who attend ANC clinics can receive all services required. Again, the rate of low-birth-weight children is very high. Therefore, there is a need for special attention from all stakeholders to reverse the trend.The district health administration and other stakeholders in health should enhance education on the importance of antenatal care services including the necessity for early antenatal care and male involvement in women's health. The health authorities should institute supply-chain management systems to prevent logistics and medicine shortages. Again, there should be a study to ascertain the actual cause of the low birth weight in the district.