PONE-D-21-37201Assessment of the consistency of health and demographic surveillance and household survey data: A demonstration at two HDSS sites in The Gambia.PLOS ONE

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The Farafenni and Basse HDSS sites are supported by Medical Research Council Unit The Gambia at London School of Hygiene and Tropical Medicine. The household survey was supported by a grant from the University of Nagasaki, Japan. 

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The Farafenni and Basse HDSS sites are supported by Medical Research Council Unit The Gambia at London School of Hygiene and Tropical Medicine. The household survey was supported by a grant from the University of Nagasaki, Japan. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Additional Editor Comments:

This is a very important report on the design and methodology challenges of childhood mortality in a low middle-income country. The authors leverage their 2 Health Demographic Surveillance System (HDSS) capturing births, deaths and migration for at least a decade. On these sites, they applied a DHS-like survey for around ~2500 households and collect full birth history for women between the ages 15 to 49 years old. Population enumeration and childhood mortality (neonatal, infant and child mortality) are compared through proportion coverage, proportion differences and rate ratios.

Issues:

1. Please add more information some demographic information of these sites: eg: Education level, and patterns of migration.

2. Provide some information about the enumerators/interviewers (training, schooling level and ages).

3. The childhood mortality here is reported as neonatal, infant, child and under-5 mortality rates. These measures although standard there is still room for confusion. Please add a definition for each somewhere in the methods section.

4. Statistical methods:

- The authors used the Kaplan-Meier method to estimate the mortality rates. This is fine. However, the DHS surveys use discrete survival analysis by computing monthly mortality probabilities. Then taking cumulative survival for the periods of interest. This analysis can be presented in the supplementary materials.

- A critical advantage that the authors throw away is the household pairing on HDSS and on the survey. For example, the exact number of discrepancies on household size could be computed (Table 1) rather than, first, building categories of household sizes and then doing a crosstab. As a result differences within 10 units are considered 0.

- Moreover, the whole analysis happens as if the independence assumption is fulfilled. I am OK with the current procedure but the authors should be aware that we have matched households here and perhaps this should be discussed.

5. Can you provide some description of the respondents? Are they differences between those who respond to the HDSS and those who responded to the DHS-like survey for the enumeration of the household members?

6. The date of birth issue on the survey should be reported in the results. Not too late as just one element of the discussion.

7. For all tables please change the separator of 95%CI. The dash causes confusion with the negative numbers on differences in table 2.

8. Table 3 and Table 4:

- Some of the rate ratios are not replicable. For example among the period 2011-2015 the neonatal mortality rate ratio 31.8/13.9 = 2.29 not the current 2.36

- Please add on the footnote how the chi-squared was computed.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. 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: Yes

Reviewer #2: No

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

Reviewer #1: Yes

Reviewer #2: Yes

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3. Have the authors made all data underlying the findings in their manuscript fully available?

The 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.

Reviewer #1: Yes

Reviewer #2: Yes

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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: Yes

Reviewer #2: Yes

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5. Review Comments to the Author

Please 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. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: This is a well-written manuscript for a well-designed study to assess the data quality of HDSS in two sites in the Gambia. The assessment adopted a DHS-like household surveys. I do not have specific comments on the contents of the manuscript since it is good as it is. I would just recommend if the manuscript can be reviewed by an editor to check the flow and for any typos.

Reviewer #2: I have identified several technical problems and one research design problem. The technical problems are as follows:

- The tests were conducted in a very conventional way, with the null hypothesis H0 strictly defined as Ps = Ph. However, one could allow a margin of, say, 5% around each estimate. Would a difference of 5% or 10% still be considered a significant difference? The authors could consider using the concept of Smallest effect size of interest (SESOI; see articles by Daniël Lakens). Without such a cut-off point, any small p-value with large samples could be overly interpreted as significant, and large p-values with small samples could be overly interpreted as no difference. In other words, taking the CI into account in addition to the p-value could lead to more nuanced results.

- I did not understand the explanation (lines 263-264, p 11) about the specific annual analysis conducted on the "most recent-5-year period, which is the only period fully covered by the two data collection methods". Table 2 and Figure 2 provide results from both data sources over the period 2001-2015 (Farafenni) or the period 2006-2015 (Basse), so why focus on the period 2011-2015? Accuracy of data? Avoiding recall bias?

- The reader is led to believe that the HDSS data collection misses a number of children, but then the reader discovers a huge bias in the survey data. At the very end of the discussion, a very important methodological limitation of the survey is pointed out (lines 349-350; p14): "the poor quality of date-of-birth information collected by the household survey", leading to "the significantly exaggerated propotions aged 0" in both sites. Why were these cases not cleaned up before analysis? How were these survey records treated in the mortality analysis?

The question of research design is related to the very purpose of the analysis. Mothers' IDs were matched between the 2 sources but not children's IDs (lines 174-175, p 7): this is a serious limitation of the comparison exercise, preventing the identification of children who were missed, or events that were displaced, in either source. In fact, neither the survey nor the HDSS can be considered the most reliable source. However, the authors want to test whether the survey can be used to assess the data quality and reliability of the HDSS data (lines 278-279: p 12). To this question, given the limitations of the survey and the lack of matching of children, the answer is clearly no, even before any comparative analysis is done. Thus, I disagree with the conclusion (lines 344-345; p14) that "the results clearly demonstrate that an adapted DHS-type survey can be used to validate routine HDSS data". The authors mention another, more nuanced objective, which is to check whether the two sources "adequately match each other, with one hence validating the other" (lines 286-287; p12), but I doubt that they can even achieve this objective. I am fairly confident that some of the difference between the two sources is explained by operational definitions of residence and by missed and misplaced events due to the pitfalls of the two data collection procedures, but without a more rigorous comparison it is very difficult to determine which data collection bias leads to which bias in the mortality indicators.

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Reviewer #1: No

Reviewer #2: No

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Assessment of the consistency of health and demographic surveillance and household survey data: A demonstration at two HDSS sites in The Gambia.

PONE-D-21-37201R1

Dear Dr. Jasseh,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Kind regards,

Orvalho Augusto, MD, MPH

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #2: All comments have been addressed

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2. 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 #2: Partly

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

Reviewer #2: Yes

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4. Have the authors made all data underlying the findings in their manuscript fully available?

The 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.

Reviewer #2: Yes

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5. 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 #2: Yes

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6. Review Comments to the Author

Please 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. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #2: Thank you for addressing most of my comments and suggestions. I now understand better the matching on HH ID only, given that the purpose was not to explain biases by comparing differences between the two sources.

However, I am still not comfortable with the conclusion: "This study attempts to ascertain whether a DHS-type cross-sectional household survey can be used to assess the quality of data and reliability of a prospective demographic surveillance system." To me, this means that a DHS-type survey can validate HDSS data, while the following sentence says something different: "...determining if the two different methods of data collection applied to the same population yield similar measures in terms of size, structure and commonly derivable childhood mortality indicators." The two sources could equally be right... or wrong (or anything in-between), while the biases of the two methods could balance out to yield the same results. Also, to say later that the two sources "adequately match each other" is not the same as "one validate the other".

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Reviewer #2: No

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