Comparison of Micro-Census Results for Magarya Ward, Wurno Local Government Area of Sokoto State, Nigeria, with Other Sources of Denominator Data
, Idongesit Nta Wilson 2, Brian Kaplan 1, Ndadilnasiya Endie Waziri 2, Adamu Sule 2, Halimatu Bolatito Ayanleke 2, Faruk Namalam 3, Shehu Ahmad Tambuwal 4, Nuruddeen Aliyu 2, Umar Kadi 2, Omotayo Bolu 1, Nyampa Barau 2, Mohammed Yahaya 2, Gideon Ugbenyo 2, Ugochukwu Osigwe 2, Clara Oguji 2, Nnamdi Usifoh 2 and Vincent Seaman 5Round 1
Reviewer 1 Report
The paper describes a micro-census enumeration methodology applied in Magarya Ward, Nigeria, focusing on children population to improve immunization campaigns.
The proposed methodology uses satellite imagery and geocoding to plan and guide the enumeration teams, focusing on reducing duplicate-counting and missed buildings.
Software tools are developed to assist the enumeration teams, collect the data, and perform data consolidation and data quality assessment.
The obtained results are compared with other data sources, finding that most of them overestimate the number of inhabitants.
The paper is well written and easy to read, but I suggest some changes to improve its readability further. Although I know that in some science fields it is customary to present the results before describing the methods, in this work, I find that some info is needed beforehand. Consider moving contents and avoiding redundancy, or maybe adding cross-references in the following cases:
- Section 2.1 presents the demographic analysis, but the enumeration units are shown in Section 3.5. Adding a cross-ref may suffice.
- Section 2.2 compares the results with other data sources, but the sources are described in Section 3.8. I suggest explaining the sources before comparing results.
Also, the following sections should be improved:
- Section 3.6: please, consider adding more info on the developed collection tools (design, functionalities, etc.) Did you use existing ODK tools or create new ones? A reference to ODK is missing.
- Section 3.7: the approach to preemptively and promptly deal with data quality problems is interesting. A more detailed and systematic analysis of these problems could improve the quality of the work. How do you deal with missing values and incomplete data? How do you resolve "duplicates"? Some of this information is scattered in the paper, but I suggest to concentrate it in this section. Maybe you could describe the data quality problems that you plan to mitigate regarding data quality dimensions described in the literature (for example, "Data and Information Quality: Dimensions, Principles, and Techniques, by Carlo Batini et al. https://0-www-springer-com.brum.beds.ac.uk/gp/book/9783319241043)
- Section 3.9: should be improved. For example, which descriptive statistics did you use to identify missing values and outliers?
Finally, I suggest adding a discussion on related work to put in context the contributions of this work ( which are described briefly in Section 4.1). For example, how does this methodology relates to the one presented in https://0-ij--healthgeographics-biomedcentral-com.brum.beds.ac.uk/articles/10.1186/1476-072X-8-4?
Please, consider checking the following sentences/typos:
- Line 97: Are you using an average of averages? The range 3.6-9.2 is a little confusing.
- Line 227: "The average age in Sokoto the 2013 DHS..."
- Line 283: "GRASP collected high-resolution satellite imagery for Magarya ward on 5 April 2018." this sentence is strange.
Author Response
The first recommendation was to facilitate the article’s readability by including cross-references within the text, especially between the Results and the Methods sections. Specifically, a cross-reference to Section 3.5 has been added to Section 2.1 to provide a link to the description of the organizational units. Also, Section 3.8 has been deleted, and the description of each alternate data source has been included in Sections 2.2.1 – 2.2.5 as part of the comparison discussion with the micro-census results.
The second recommendation is to add more info on the developed
collection tools. Section 3.6 was created to describe in detail the development
of the ODK data collection form (and add a citation for this software), and the
data points it collects.
The third recommendation was to better describe the procedures used to deal with missing values, duplicate information and incomplete data. These procedures are described in Section 3.9, before listing the analyses process we developed to obtain our results.
The fourth recommendation was to gather all descriptions of data quality checks (before, during and after data collection) into a single section. Section 3.8 describes the most common data quality challenges, and lists the validity checks that were implemented for this micro-census. This section now provides a list of best practices to improve data accuracy and completeness for public health interventions in developing countries.
The fifth recommendation was to strengthen the literature review in Section 4.1, to place the methodology of this micro-census within the context of existing mapping strategies. References to relevant articles have been added to describe the use of satellite images and mapping in public health operations, and the addition of the field navigational tool that makes our intervention unique.
The final recommendations were to review the syntax of three sentences. These have been edited according to the proposed wording.
Reviewer 2 Report
The paper is interesting notwithstanding the limitations enumerated, mainly due to the limits in granularity of the Official Census data, as reported in the text.
Author Response
No specific recommendations were provided by the reviewer, who acknowledged the limitations we listed and attributed them to the limited granularity of the Official Census data
Reviewer 3 Report
The article “Comparison of micro-census results for Magarya Ward, Wurno Local Government Area of Sokoto State, Nigeria, with other sources of denominator data” presents a unique micro sensus data to highlight the discrepancies among the various methods. They use established techniques of gathering data and present the data and validation. Such data will be very useful both locally and has broader implications for understanding the biases globally. The article is very well written and within the scope of this journal. Congratulations to the authors for compiling this data and this manuscript. The data description, analysis and presentation is very thorough. My only comment is do the authors wish to make this data available free online? I will strongly recommend doing so.
Author Response
The only recommendation put forth is to make the dataset of this micro-census available online for free. The Global Immunization Division of the US Centers for Disease Control and Prevention is discussing this option with the Sokoto State Emergency Routine Immunization Coordination Center (SERICC) and the Sokoto State Primary Health Care Development Agency (SPHCDA).
Round 2
Reviewer 1 Report
The authors successfully replied to all the suggestions, and the performed changes improved the clarity and the quality of the paper.
