PONE-D-19-33574

Predictors of perinatal death in the presence of missing data: A registry-based study in northern Tanzania

PLOS ONE

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women whose information enabled availability of data used in this study. We also acknowledge

the Norwegian Council for Higher education program and Development Research (NUFU) for

funding the establishment of the KCMC Medical birth registry. This funding was not related to

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Comments to the Author

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Reviewer #1: Partly

Reviewer #2: Partly

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: No

Reviewer #2: No

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Reviewer #1: No

Reviewer #2: No

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Reviewer #1: Yes

Reviewer #2: Yes

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5. Review Comments to the Author

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Reviewer #1: Thank you for the invitation to review this manuscript, which I read with great interest. The authors used data from KCMC medical birth register, which collected information from mothers about their health during pregnancy, delivery details and characteristics of the child. The authors used these data to explore risk factors for perinatal deaths (stillbirths and early neonatal deaths), applying multiple imputation to account for missing data. The paper looks at an interesting subject, but some key information about missing data is not reported (especially given it’s the key focus of the paper), and I have some concerns about selection of variables in the model. Major revisions are required prior to publication.

Major comments:

Given that evaluation of the impact of missing data is one of the key aims of the paper, there is insufficient information about missing data, patterns and imputation. The authors should consider adding information on:

1. the patterns of missing data: how much data was missing and for which variables? the authors state in tables 1-2 that counts don’t add up to the total, but the number of pregnancies with missing data should be added. It is not clear how many children were included in the cohort overall and how many in the complete case cohort for analyses?

2. what was the impact of missing data on the perinatal mortality? It is not clear what was the perinatal mortality rate in the full cohort (including all pregnancies with missing data) compared to complete case cohort. It is unclear why the mortality rate went up after imputation (from 41.6/1000 births to 53/1000 births)

3. What was the imputation model? Which variables needed to be imputed and which were used to predict missing data?

4. Authors state in multiple parts of the manuscript that complete case analyses were biased and led to incorrect conclusions. However, the results seem to be largely consistent between complete case and imputed data, with slightly different effect sizes, but the same conclusions would be reached based on complete case data. I think such strong statements should be avoided

The authors might find these suggested reporting guidelines for papers using multiple imputation useful: Sterne JA, White IR, Carlin JB, et al. Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls. BMJ. 2009;338:b2393. Published 2009 Jun 29. doi:10.1136/bmj.b2393

My second major comment related to the modelling strategy. What was variable selection strategy? It looks that all risk factors available were included in the model, but they are likely to be collinear, eg having various pregnancy complications is probably closely correlated with referral for delivery. Having multicollinearity in the model could introduce bias to the analysis, e.g. the odd result for alcohol consumption during pregnancy (showing protective effect) could be a result of that over adjustment in presence of other unmeasured confounding. The authors should have a look at literature on birthweight paradox.

Other comments:

1. Perinatal mortality should be defined as stillbirths and early neonatal deaths per 1000 total births, including live and stillbirths (see WHO ICD10 instruction manual https://www.who.int/classifications/icd/ICD10Volume2_en_2010.pdf?ua=1), but the authors report perinatal mortality per 1000 live births

2. Abstract: explain all abbreviations as it is not obvious what they are

3. Methods: Why did the authors run models to estimate both odds ratios and risk ratios, rather than just risk ratios?

4. Methods: The authors note that mothers could chose not to participate - what proportion of mothers opted out and hence is not covered?

5. Results: are figures 1 and 2 showing percentage out of all births? Since the authors have fitted a trend, by how much did perinatal mortality decline? The results splitting perinatal mortality into stillbirths and early neonatal deaths could be discussed more – the declines were driven by early perinatal mortality as stillbirth rate remained constant, but in most recent years something has changed and early neonatal deaths went up while stillbirth went down. Why is that? Is it due to registration practices? It would be worth discussing.

Reviewer #2: Reviewed comments from Unnati Saha

The manuscript “Predictors of perinatal death in the presence of missing data: A registry-based study in northern Tanzania”

The paper focused on an important topic to address on the determinants of perinatal mortality and importance of missing observations and their attribution in the analysis. My comments are given below.

-Overall the paper is written well, but additional revision by an expert English editor is necessary for the improvement of the manuscript for publication in Plos One.

Specific comments are given as below.

Abstract:

Backgorund

-perhaps it would be complete if author(s) replace “zonal”with the complete name of the hospital e.g., Kilimanjaro Christian Medical centre (KCMC).

Methods

-In this section Author(s) probably can mention KCMC instead writing the full name of the hospital

Conclusion

- “Appropriate choice of the analysis model should be considered in similar studies”. Not clear about this sentence.

- Which one is benchmark model? Please refer the benchmark model based on results

- Also, better if author(s) revise the last two sentences under the conclusion section.

Main Body text

Overall comments

- Manuscript does not include page numbers and line numbers, which hinders to reporting reviewed comments orderly

Introduction:

- paragraph third, last sentence, I understand that the author(s) performed analysis with and without missing values imputation and compared the results for statistical significance. So, perhaps it would be better if they explain the aim of the study completely.

Methods:

Study design and participants

-second paragraph, Author(s) did not make it explicit why they excluded multiple births from the analysis

-the explanation of the method how missing values were imputed are missing, also better give a theoretical explanation of the use of two methods how one model better perform over other model.

variables

- author(s) included so many variables in the analysis, so I am wondering whether it hampers the effects of some variables on estimated perinatal mortality due to co-linearity. For example, low birthweight, gestational week, preterm birth, and other medical indicators. So, author(s) need to be cautious about entering variables in the model.

-also, as WHO recommended for 8 ANC visits (I read in the INTRODUCTION section), author(s) perhaps could try to investigate the effects of 8 visits as well.

- I miss the definition of the perinatal death (as dependent variable) whether dummy or continuous, although next section statistical analysis it gives a hint.

Statistical analysis

- first paragraph, the explanation of the method how missing values were imputed are missing.

- Model I (‘binomial family’ means outcome variable is coded as dummy), Model II (log-linear regression with passion family) means outcome variable is count variable. How they handle the 0 counts (no perinatal deaths) in the analysis.

- Author(s) performed two models using two types of outcomes. Author(s) could play with variables in different models of both outcome variables “perinatal mortality”, e.g., child, mother, family, clinical etc. different levels of variables. The results could provide better understanding of the effects on the model outcome.

- author(s) applied GEE method, but as author(s) expect multiple deaths born to a mother, so how the analysis handled the correlated deaths in one family (mother)?

- if deaths are correlated and not accounted for this, it may spurious the conclusion on determinants of perinatal deaths. Author, may give explanation on this.

- again, perhaps author(s) can explain imputation technique in better way for general readers.

Results:

Maternal background and characteristics at first birth

- Why first birth? I understand that author(s) analyzed perinatal mortality that occurred at any birth born to a mother in the hospital and also in rural area referred to this hospital

- Few cases (<=.5%) are recorded against variables PPH, Abruption placenta, placenta previa, so I am wondering whether entering those variables in the model will be appropriate. It may miss-specify the model. Also, better if there is an explanation how those variables can effect pregnancy and mortality e.g., PROM.

- Also check all variables distribution including alcohol drinking for the referral cases as analysis was stratified by referral vs not referral cases.

- Table 3, should it be “maternal characteristics”? revise the title

Predictors of perinatal death

-Table 4, n should be different before and after imputation, so better to mention them

- do the author(s) have any explanation how alcohol consumption may reduce perinatal deaths (see Table 4)? Also, explain the results of alcohol consumption in model 5 for referral cases vs. not referral cases.

-Table 5, which model employed for this analysis? Seeing the sample size it seems model with imputation data, but not clear which model (from the title)? Model I or Model II?

- Seeing Table 5 and coefficients of alcohol use it seems to me that better exclude this variable from the analysis. The effects are different for referral and not referral, and that is the reason that in Model II in Table 4, after imputation the effect was lower and insignificant.

- Table 6, again correct the number of cases

- Women had complication and that is why probably they had CS delivery. It’s effect may affect the effects of referral including other complication variables.

- Better to add a section explaining model and better fitted model

- Discussion section requires careful revision with proper explanation of the effects with references and conclusion drawn from this study.

I would suggest to accept the paper after incorporating the suggested comments indicated as above of this note.

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Reviewer #1: No

Reviewer #2: No

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Predictors of perinatal death in the presence of missing data: A registry-based study in northern Tanzania

PONE-D-19-33574R1

Dear Dr. Mboya,

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