Amendments from Version 1

F1000Research

2046-1402

F1000 Research Limited

London, UK

10.12688/f1000research.73822.2

Data Note

Articles

Curating maternal, neonatal and child health (MNCH) datasets from a hospital’s catchment area in Nigeria between 2014 and 2019

[version 2; peer review: 1 approved, 2 approved with reservations]

Ekpenyong

Moses Effiong

Conceptualization Data Curation Methodology Project Administration Supervision Validation Writing – Original Draft Preparation Writing – Review & Editing https://orcid.org/0000-0001-6774-5259 a 1 2 Usip

Patience Usoro

Conceptualization Data Curation Funding Acquisition Methodology Project Administration Supervision Writing – Original Draft Preparation 2 Usang

Kommomo Jacob

Data Curation Funding Acquisition 2 Umoh

Nnamso Michael

Data Curation Funding Acquisition Investigation Methodology Validation Writing – Original Draft Preparation Writing – Review & Editing 2 Oyong

Samuel Bisong

Data Curation Investigation Validation Writing – Original Draft Preparation 2 Nwokoro

Chukwudi Obinna

Data Curation Investigation Validation Writing – Original Draft Preparation 2 Suleiman

Aminu Alhaji

Data Curation Investigation Validation Writing – Original Draft Preparation https://orcid.org/0000-0003-3270-219X 3 Attai

Kingsley

Data Curation Formal Analysis Methodology Validation Writing – Original Draft Preparation https://orcid.org/0000-0002-2199-5049 4 John

Anietie Emmanuel

Data Curation Investigation Validation Writing – Original Draft Preparation 2 Clement

Inyang Abraham

Data Curation Investigation Validation Writing – Original Draft Preparation 2 Johnson

Ekemini Anietie

Data Curation Investigation Validation Writing – Original Draft Preparation 2 Fakiyesi

Temitope Joel

Data Curation 2 1Centre for Research and Development, University of Uyo, Uyo, Akwa Ibom, 520003, Nigeria 2Department of Computer Science, University of Uyo, Uyo, Akwa Ibom, 520003, Nigeria 3Department of Computer Science, Abdu Gusau Polytechnic, Gusau, Zamfara, Nigeria 4Department of Computer Science, Rittman University, Ikot Ekpence, Akwa Ibom, Nigeria

a mosesekpenyong@uniuyo.edu.ng

No competing interests were disclosed.

11 9 2023

2022

166

6 9 2023

2023

This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

In this Data Note, we present details regarding Maternal, Neonatal, and Child Health (MNCH) datasets sourced directly from patients' medical records. These datasets consist of 538 maternal, 720 neonatal, and 425 child records, all collected at St Luke’s General Hospital in Anua, Uyo, Nigeria, spanning from 2014 to 2019. Variables included in the datasets are: Maternal {patient number, date of visit, gender, age, class of patient, address, LGA, diagnose, symptom, prescription, blood pressure (mm Hg), temperature (degree centigrade), weight (Kg), latitude, longitude, elevation, (MSL), date record, GPS Accuracy (m)}; Neonatal {patient number, date of visit, gender, age, class of patient, address, LGA, symptom, health status, height (cm), weight (Kg), latitude, longitude, elevation (MSL), date record, GPS Accuracy (m)}; and Child Health {patient number, date of visit, gender, age, class of patient, address, LGA, diagnose, health history, temperature (degree centigrade), weight (Kg), latitude, longitude, elevation (MSL), date record, GPS accuracy (m)}. The purpose of sharing these datasets is to provide a resource for researchers interested in their potential reuse, whether for analysis, research, quality assurance, policy formulation, decision-making, patient safety, or other purposes. The datasets also include location information obtained through GPS (Global Positioning System) data from the study area, facilitating spatiotemporal analysis. We outline the methods used for curating the datasets, including the protocol for selecting and processing variables. To protect patient privacy, certain personal details such as names were replaced with unique patient numbers generated using Microsoft Excel. Furthermore, specific patient information, including addresses/locations, date of visit, latitude, longitude, elevation, and GPS accuracy, has been restricted for privacy reasons. Readers interested in accessing restricted data can make a formal request to the corresponding author (see data restriction statement). The curated datasets are available at the Open Science Framework.

context-aware system robust decision support GeoAI healthcare indicator location-based information MNCH data

Tertiary Education Trust Fund

This work was supported by The Tertiary Education Trust Fund (TETFUND), Nigeria.

The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Revised Amendments from Version 1

Slight modification of the paper title to reflect the present domain of study. Discussion of software tools for managing and visualizing MNCH datasets. Inclusion of specific research objectives and finetuning of the research hypotheses. Inclusion of a location map describing the study location the datasets were collected. Inclusion of established protocols for maintaining the confidentiality and anonymity of patients' health records and to mitigate the risk of inadvertently identifying specific individuals within the local community.

Introduction

Access to health services is essential for promoting health equity and quality of life ( dos Anjos Luis & Cabral, 2016). Hence, knowledge about available MNCH facilities is essential for making informed decisions in health planning. Moreover, available healthcare datasets, like the one presented in this publication, reveal that some patients have to travel long distances to access healthcare facilities. In urban areas, there is a notable imbalance in the patient-to-provider ratio, with a significant concentration of the patient population per healthcare facility. Oleribe et al. (2019) identified major factors hindering access to quality healthcare in sub-Saharan Africa, including financial barriers, poor governance, and limited infrastructure. In Nigeria identified hidering factors include, financial constraints due to poor living conditions, heightened security threats affecting healthcare access, the type and nature of ailments influencing healthcare choices, geographic residence impacting access, racial and ethnic disparities affecting healthcare outcomes, gender-related disparities, age-related healthcare considerations, language barriers affecting communication, and disability-related healthcare challenges. These factors significantly influence the utilization of medical and healthcare services in terms of availability, timeliness, convenience, and affordability ( Babalola & Fatusi, 2009). The integration of modern technology into the health sector has simplified healthcare services. For instance, the integration of electronic health records and predictive intelligence (e.g., smart technology) into healthcare services have achieved efficient, accurate storage and retrieval of patients’ records, as well as intelligent data-driven analysis, prediction, and visualisation ( Tian et al., 2019).

Unlike developed nations, health facilities in low- and medium-income countries such as the sub-Sahara African region are overly stressed, generating a large pool of manually unstructured and inconsistent data; defying efforts to extract meaningful insights, hinder accurate decision-making, and impede the creation of reliable healthcare solutions and strategies. Furthermore, the reduced health budget has decreased government’s efforts in establishing new healthcare centres to equate the present population growth, hence, increasing the establishment of privately owned healthcare centres, premised on business and which services are not conducive to patients in terms of cost. Even though availability of health facilities is often prioritised over accessibility by decision makers ( Tuba et al., 2010), Mishra et al. (2019) maintained that geographic accessibility and availability of healthcare facilities are essential parameters in determining the quality of care received, as analysis of both parameters could reveal useful patterns and trends for providing a more robust health system that derives patient-centred care. Patient-centred care ( Epstein & Street, 2011) empowers patients to actively participate in their care with physicians and other healthcare providers connecting with patients to effectively address patients’ needs. To achieve this, it's essential that technology-driven systems, aiming to enhance the utility of healthcare delivery systems, prioritize the availability of real-time location-based information and comprehensive details of the healthcare system. Also, collaboration between the necessary stakeholders (physicians and other healthcare providers, government, patients) is necessary and can be enabled using geospatial artificial intelligence (GeoAI) ( Boulos et al., 2019; VoPham et al., 2018). GeoAI combines spatial science methods such as GIS (geographic information system), data mining, and high-performance computing to extract meaningful knowledge from spatial big data ( Janowicz et al., 2020; VoPham et al., 2018).

Geospatial software tools for managing and visualizing MNCH datasets include:

ArcGIS: Developed by Esri (Environmental Systems Research Institute) is a comprehensive and widely used GIS software suite, designed for managing, analyzing, visualizing, and sharing geospatial data and information.

QGIS (Quantum GIS): An open-source and user-friendly GIS software that provides powerful mapping and data analysis capabilities.

GRASS GIS (Geographic Resources Analysis Support System): An open-source GIS software that focuses on geospatial data management, analysis, and visualization.

R with Spatial Packages: The R programming language, coupled with specialized spatial packages like "sp," "sf," and "raster," can be used for geospatial data analysis and visualization.

GeoDa: A free software program designed for exploratory spatial data analysis, spatial statistics, and spatial econometrics.

SAGA GIS: An open-source geographic information system with a wide range of geospatial analysis and modeling tools.

Mapbox: A cloud-based platform for designing and publishing custom maps, providing tools for geospatial data visualization and analysis.

OpenStreetMap (OSM) Tools: Tools and APIs for working with OpenStreetMap data, which can be useful for incorporating community-contributed geographic data.

Google Earth Engine: A cloud-based platform for analyzing and visualizing Earth observation data, which can be valuable for spatiotemporal analysis.

The choice of geospatial software however depends on factors such as, data requirements, project goals, budget, and end-users’ familiarity. Each of these software options offers unique features and capabilities for working with geospatial data.

This publication creates a maternal, neonatal, and child health (MNCH) datasets directly sourced from patients' medical records for a data poor setting. The curated datasets are instrumental in facilitating driving location sensitive decision making, intelligent health data mining, informed policy planning, and robust decision support systems design. The specific objectives of the research therefore include:

To gather and compile detailed MNCH data from various sources to create a comprehensive dataset.

To develop a standardized MNCH data and structure format.

To convert unstructured healthcare data into a semi-structured format, making it suitable for analysis.

To integrate location-based information into the MNCH dataset for facilitating spatiotemporal analysis and visualization.

The following are the study’s hypothesis:

Technology driven data curation practices improves MNCH outcome predictions

Integration of location-based data into MNCH datasets leads to more informed policy insights and recommendations for MNCH.

Geo-referenced features of MNCH datasets enables real-time demographic/spatiotemporal analysis.

Usip et al. (2021) used the MNCH datasets, to develop a parser with preposition recognition capabilities and extract prepositions from clinical notes for unstructured patient data visualization, incorporating generated location items like noun phrases, geolocations, and place names.

Materials and methods Ethical approval

Ethical clearance was granted by the University of Uyo Health Research Ethics Committee (UNIUYO-HREC) – Ref. number: UU/CHS/IHREC/VOL.I/017 with the acceptance that the study did not require direct contact with patients.

Data source, sample size and capturing procedure

The source of the datasets is patients’ medical records/files retrieved from the St Luke’s General Hospital, Anua, Uyo, Akwa Ibom State, Nigeria (the healthcare facility). St Luke’s General Hospital, Anua is one of the foremost Missionary Hospitals in the South-South and South-East Nigeria. The hospital is located along Nwaniba Road in Uyo Metropolis of Akwa Ibom State, Nigeria, West Africa. A location map showing the approximate hospital catchment for which the datasets are available is presented in Figure 1.

Figure 1. Location map showing St Luke’s General Hospital, Anua.

These records cover the period from 2014 to 2019. The process of selecting properly documented files involved physically inspecting the patients’ records, which were manually stored in the hospital’s file cabinets or archives. To initiate this procedure, we sought informed consent through the Chief Medical Director of the hospital to obtain the necessary data. After obtaining permission, the necessary arrangements were made to commence the exercise. It’s important to note that the investigators did not have direct access to the records room or the patient archives. Only files preselected by the designated officers assigned by the Chief Medical Director of the hospital were made available for the investigators’ use. Before handing over these files for the capture of attributes, the assigned officers reviewed them to ensure compliance with the primary attributes of the study, as outlined in Table 1).

Table 1. Description of maternal, neonatal and child health (MNCH) data capture template.

Attribute	Description
Date of visit	Date patient visited the hospital
Gender	Gender of patient
Age	Age of patient
Class of patient	Age classification (Mother, Infant or Child)
Address	Home address or location of the patient
Symptom	The cause of the ailment
Diagnosis	Outcome of the examination of patient
Prescription	Administered therapy/drug
Health history	Health history of patient
Health status	Health status of patient (Apgar score), ( Finster et al., 2005)
Blood pressure	Blood pressure of patient in millimetre per mercury (mm/Hg)
Temperature	Temperature of the patient in degree centigrade (°C)
Height	Height of patient in centimetres (cm)
Weight	Weight of patient in kilogrammes (Kg)

To capture the primary attributes for maternal, neonatal and child health, a data template (a table with attributes of the study), was designed with ethical considerations in mind. Protocols established to maintain the confidentiality and anonymity of patients' health records and to mitigate the risk of inadvertently identifying specific individuals within the local community include:

Data De-Identification: Personal identifiers, such as names, were systematically removed or replaced with codes or pseudonyms to prevent any association between the data and specific individuals.

Location Privacy Measures: Specific location/address details, including street numbers, were redacted or generalized to a broader geographic level to minimize the risk of identifying individual patients based on their addresses.

Aggregated Data Analysis: Instead of analyzing individual-level data, the analysis was conducted at an aggregated or group level whenever possible. This approach ensures that results and insights do not pertain to any single person but are generalized across a broader population.

Access Control: Access to the dataset and any related information was restricted to authorized personnel only, and strict confidentiality agreements were in place to prevent any unauthorized disclosure of patient identities.

Ethical Review and Compliance: The study and its data-handling procedures were subject to ethical review and compliance with relevant privacy and data protection regulations and guidelines to ensure patient anonymity.

Maternal health data template had the following attributes (Date of visit, Gender, Class of patient [mother/infant/child], Address, Symptom, Diagnosis, Prescription, Blood pressure, Temperature, Weight). Neonatal health data template had the following attributes (Date of visit, Gender, Age, Class of patient, Address, Symptom, Condition, Height, Weight). Child health data template had the following attributes (Date of visit, Gender, Age, Class of patient, Address, Diagnosis, Health history, temperature, Weight). The description of these attributes is tabulated on Table 1.

The total sample of data retrieved (before processing) included maternal (1063), neonatal (1367) and child patients (826), covering the 3 senatorial districts of Akwa Ibom State namely Uyo, Ikot Ekpene and Eket, and the 31 local government areas (LGAs) as presented on Table 2.

Table 2. Senatorial districts and local government areas of captured data.

LGA=local government area.

Senatorial Districts	LGA	Number of LGA
Uyo	Uyo, Itu, Uruan, Etinan, Ibiono Ibom, Nsit Ibom, NsitUbium, Nsit Atai, Ibesikpo Asutan	9
Eket	Eket, Ikot Abasi, Mkpat Enin, ONNA, Eastern Obolo, Esit Eket, Ibeno, Okobo, Mbo, Oron, Udung Uko, Urue Offong Oruko	12
Ikot Ekpene	Ikot Ekpene, Abak, ObotAkara, Ika, Ukanafun, Etim Ekpo, Ini, Ikono, Oruk Anam	10
	Total:	31

Geolocation capture and data processing

To enable the support of GeoAI services, additional attributes were collected by visiting the respective study locations. The visited locations were those associated with the collected data. The UTM Geo Map, a simple android application for coordinates capture, GIS, and Spatial analysis was deployed for this purpose. The UTM Geo Map app can be downloaded from the Google play store, and has several modules, but the Map Coordinates module, which maps coordinates in real-time was used to capture the respective location coordinates. The process for obtaining the location coordinates (latitude and longitude) using the UTM Geo Map app are summarised as follows:

Step 1: Launch the UTM Geo Map app when in the vicinity of patient address

Step 2: Select Map Coordinates

Step 3: Select Goto GPS Location (this step gives the real-time location of the mobile device with GPS accuracy in meters appearing on the screen. Ensure that the GPS accuracy is within an acceptable range).

Step 4: Select Mark. A request to enter the Point Name will pop up. Enter the point name or address of the patient

Step 5: Select Save. Each saved point is stored on the mobile device. To transfer the measured data to an external file, there is an Export tool, which supports different file formats such as KML, CSV, GPX, DXF, TXT, GeoJSON.

Step 6: Select Export/Import, Export to CSV, type in a filename with “.csv” extension.

Step 7: Select Save.

The exported file format used in this publication is the CSV format, and the columns (attributes) extracted are described in Table 3. A GPS accuracy range of 1 – 9.65 metres (i.e., how close the device’s calculated position is from the truth, expressed as a radius), was used as an acceptable accuracy range for this publication. A lower GPS accuracy defines the precision of the patient location. The coordinates capturing was carried out by doctoral students, using different mobile devices. Where the GPS accuracy was too high, such location was recaptured and tuned to the acceptable accuracy range. Due to ethical reasons, we are only interested in the vicinity of the patient, hence the defined accuracy range.

Table 3. Extracted attributes of location coordinates.

Attribute	Description	Sample data	Data type
ID	Identity or point	PT_4 Etuk Allan street itam	Alphanumeric
Latitude	Latitude is the angle ranges from 0° at the Equator to 90° (North or South) at the poles	5.0437963	Numeric
Longitude	Longitude is the measurement east or west of the prime meridian (0–180°) East or west	7.8936366	Numeric
Notes	Descriptive	Null	Alpha
DMS	Degrees, minutes, and seconds	5Â° 2’ 37.67’’ N \| 7Â° 53’ 37.09’’ E	Alphanumeric
UTM	Universal Transverse Mercator	377355.436E557609.59N32N	Alphanumeric
MGRS	Military Grid Reference System	32NLL 77355 57610	Alphanumeric
CRS	Coordinate Reference System	7.8936366 5.0437963	Numeric
CRS Code	Coordinate Reference System code	EPSG:4326	Alphanumeric
Elevation (MSL)	Elevation of Mean Sea Level	69.46	Numeric
Address	Location	Null	Alphanumeric
Date Record	Capture date	Record Date: 2021-05-20 11:11:02	Numeric
GPS Accuracy (m)	Global Positioning System (GPS) Accuracy	3.900000095	Numeric
Photo	Picture of the location	Null	Image

To clearly mark the location boundaries of patients and geographically localise them within a local government unit, the address column was further split to form an additional attribute, called the LGA. Location attributes documented as part of the datasets include Latitude, Longitude, Elevation, Date recorded, and GPS accuracy. For this publication, we were only able to provide location data for patients within the Uyo metropolis, hence, resulting in a total of 1683 MNCH records and distributed as follows: maternal=538, neonatal=720, child=425. We hope to cover other senatorial districts as soon as future funding is available.

At the end of the data capturing exercise, the data template was converted into electronic format using Microsoft Excel, and manually merged with the geolocation records (exported CSV file) from the field (or study locations visited). The first 10 samples of the maternal, neonatal and child health datasets are given in Figure 2, Figure 3, and Figure 4, respectively. The dataset can be found as Underlying data ( Ekpenyong et al., 2021).

Figure 2. Sample maternal health dataset.

LGA=local government area.

Figure 3. Sample neonatal health dataset.

LGA=local government area.

Figure 4. Sample child health dataset.

LGA=local government area.

Data availability Underlying data

Open Science Framework: Maternal, Neonatal and Child Health Datasets for Spatiotemporal Data Analytics. https://doi.org/10.17605/OSF.IO/J9ZH8 ( Ekpenyong et al., 2021).

Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).

GPS data

Access to restricted data (GPS data) will be made available to readers after a formal request to the corresponding author ( mosesekpenyong@uniuyo.edu.ng) and on the condition that data will be used strictly for research purposes.

Acknowledgements

We extend our gratitude for staff of the St Luke’s General Hospital, Anua, and participating doctoral students, for enabling a smooth data capture of the MNCH datasets.

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: The Apgar score has survived the test of time. Anesthesiology. 2005;102(4):855–857. 15791116

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: GeoAI: spatially explicit artificial intelligence techniques for geographic knowledge discovery and beyond. Int J Geogr Inf Sci. 2020;34(4):625–636. 10.1080/13658816.2019.1684500

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: Geo-spatial site suitability analysis for development of health care units in rural India: Effects on habitation accessibility, facility utilization and zonal equity in facility distribution. J Transp Geogr. 2019;78:135–149. 10.1016/j.jtrangeo.2019.05.017

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10.5256/f1000research.155709.r433364

Reviewer response for version 2

Bonnet

Emmanuel

1 Referee https://orcid.org/0000-0001-6735-5330 1Institut de Recherche pour le Développement, CNRS Université Paris 1 Panthéon- Sorbonne, Île-de-France, France

Competing interests: No competing interests were disclosed.

11 12 2025

2025

This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

recommendation

approve-with-reservations

My assessment of this manuscript after the implementation of the previous reviewers’ recommendations, I acknowledge that substantial progress has been made between version 1 and version 2. The objectives are now clearly stated, the hypotheses have been reformulated in a more coherent and scientifically defensible manner, and the ethical safeguards regarding data anonymization and confidentiality are now described in a much more rigorous and explicit way.

The inclusion of a location map, the enrichment of the discussion on geospatial software beyond ArcGIS alone, as well as the clarification of data availability via the OSF platform, constitute improvements that respond to the main methodological requests made by the initial reviewers.

However, despite these advances, several important weaknesses remain unresolved and should be corrected before the manuscript can be considered fully mature. I believe that the authors, as specialists in cartography and geospatial data, could do better than providing a simple screenshot and a rough sketch for the location map. In particular, essential cartographic elements such as a scale and other mandatory components are missing.

The most critical unresolved issue concerns GPS accuracy. Although the authors explicitly stated in their response to the first round of reviews that the numerical GPS precision range (1–9.65 metres) would be removed, this information is still present in the Methods section of version 2.

A second major limitation concerns the spatial restriction of the dataset to the sole metropolis of Uyo. Although lack of funding is mentioned as a justification, the manuscript still does not provide a clear methodological rationale for this choice in terms of representativeness, sampling logic, or implications for the generalizability of the results.

Although the introduction has been partially revised, it is still too long and continues to contain a substantial proportion of general statements on technology and health systems that are only indirectly related to the specific contribution of this dataset. A stronger refocusing on the originality, structure, and concrete applications of the MNCH dataset would significantly strengthen the manuscript.

Although the temporal structure of the data is stated to be available via the OSF repository, it remains only minimally explained within the manuscript itself, even though spatio-temporal analysis is presented as one of the major intended uses of the dataset.

In conclusion, version 2 reflects a genuine effort to take previous reviewers’ comments into account and shows notable improvements regarding the objectives, hypotheses, ethics, and data access. Nevertheless, the inconsistencies mentioned above still limit the full scientific robustness of the manuscript. A further targeted revision addressing these specific points is required to reach a level fully consistent with the standards expected for durable international indexing.

Are sufficient details of methods and materials provided to allow replication by others?

Yes

Is the rationale for creating the dataset(s) clearly described?

Yes

Are the datasets clearly presented in a useable and accessible format?

Yes

Are the protocols appropriate and is the work technically sound?

Yes

Reviewer Expertise:

Health geographer, geo-epidemiologist

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.

10.5256/f1000research.155709.r205335

Reviewer response for version 2

Macharia

Peter M.

1 2 Referee https://orcid.org/0000-0003-3410-1881 1Department of Public Health, Institute of Tropical Medicine Antwerp, Antwerp, Belgium 2Population & Health Impact Surveillance Group, Kenya Medical Research Institute-Wellcome Trust Research Programme, Nairobi, Kenya

Competing interests: No competing interests were disclosed.

16 10 2023

2023

recommendation

approve

By a location map I meant,

Map out the approximate hospital catchment for which the datasets are available.

The map should also show other facilities in the vicinity,

Place the location of the map within the boundaries of the country as an inset smaller map.

Are sufficient details of methods and materials provided to allow replication by others?

Yes

Is the rationale for creating the dataset(s) clearly described?

Yes

Are the datasets clearly presented in a useable and accessible format?

Partly

Are the protocols appropriate and is the work technically sound?

Yes

Reviewer Expertise:

Geospatial health/spatial epidemiology

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.

10.5256/f1000research.155709.r205336

Reviewer response for version 2

Makacha

Liberty

1 2 3 Referee https://orcid.org/0000-0002-2360-3969 1School of Public Health, Imperial College London, London, England, UK 2Women and Children's Health, King's College London, London, England, UK 3Surveying and Geomatics, Midlands State University, Gweru, Midlands Province, Zimbabwe

Competing interests: No competing interests were disclosed.

16 10 2023

2023

recommendation

approve-with-reservations

Overall, the manuscript presents valuable data related to Maternal, Neonatal, and Child Health (MNCH) records collected from St Luke's General Hospital in Anua, Uyo, Nigeria. The dataset is aimed at facilitating research, analysis, policy formulation, and decision-making in the field of healthcare, particularly in low- and medium-income countries. The authors provide comprehensive details regarding the dataset's collection and ethical considerations. However, there are areas that still need improvement before considering it fit for indexing.

Review with recommendations:

Abstract:

The abstract provides a clear and concise overview of the study, the dataset, and its purpose.

Introduction:

The introduction highlights the importance of MNCH data and its implications for healthcare planning effectively.

However, the introduction is still quite lengthy, and some parts could be condensed to maintain reader engagement.

Materials and Methods:

The ethical approval section is well-detailed and essential for transparency.

The data source and capturing procedure are clearly explained, including the steps to maintain data privacy.

It is unclear why the dataset collection is limited to the Uyo metropolis. The rationale for this should be clarified.

Data Availability:

The manuscript correctly provides information about where the dataset is available and the conditions for access.

The use of a Creative Commons Attribution license is appropriate for data sharing.

Language and Style:

The manuscript mostly follows a clear and concise writing style.

Some sentences are overly complex and could be simplified for better readability.

Figures and Tables:

Figures and tables are well-structured and contribute to understanding the text.

The manuscript should include more detailed explanations for certain figures, particularly Figure 1, to enhance reader comprehension.

Recommendations:

The introduction section needs to be streamlined, with a focus on the core objectives and research questions of the study.

Consider expanding the discussion on the implications and potential applications of the MNCH dataset. How can this data be used for policymaking, patient care, or research in low- and medium-income countries?

Clarify the reasons for focusing exclusively on the Uyo metropolis for data collection and provide a plan for potential expansion to other areas.

Overall, this manuscript presents an important dataset and research on MNCH in a low- and medium-income country context. With some revisions to improve clarity and a more concise introduction, it could be in a indexable state. Additionally, consider engaging with relevant research literature to highlight how this dataset contributes to the field.

Are sufficient details of methods and materials provided to allow replication by others?

Yes

Is the rationale for creating the dataset(s) clearly described?

Are the datasets clearly presented in a useable and accessible format?

Yes

Are the protocols appropriate and is the work technically sound?

Partly

Reviewer Expertise:

Geographic Information Systems, Health Geography or Spatial Epidemiology

10.5256/f1000research.77502.r173253

Reviewer response for version 1

Makacha

Liberty

Competing interests: No competing interests were disclosed.

31 5 2023

2023

recommendation

approve-with-reservations

The objective of this study is to create a publicly available geographically precise dataset for a data poor setting that will enable driving location sensitive decision making. The authors collected patient data and linked it to participants precise locations (home address). The article would benefit more if the authors seek grammar review from experts in the the field. Whilst the manuscript presence an important argument for data poor settings, this did not come out elaborate in the current phrasing of a bigger part of the current version of the manuscript.

........ Hence, knowledge of health facilities is crucial for providing informed health planning decisions.

The statement may need to be qualified. What knowledge of health facilities are the authors referring to here?

.......with the urban areas holding very high concentration of patients’ population per facility.

.....as major factors mitigating access to quality healthcare

I would use technical terms like patient to provider ratio to describe the same.

mitigating factors: finance (poor living condition), increased security threat, type and nature of ailment, geography of residence, race and ethnicity, gender, age, language, and disability . These factors significantly determine the degree at which medical/healthcare services – including availability, timeliness, convenience, and affordability, are utilised

What are mitigating factors?

Adoption of modern technology has however simplified healthcare services through the implementation of automated systems.

What do the authors mean by this statement? Consider rephrasing for clarity.

.....that defies real-time, patient-centred care .

Again this statement may require clarification. How does unstructured data defy patient-centred care?

..... and details of every parameter within the health system should be available in real-time.

In my opinion this statement may need to be qualified as its overly ambitious in implication for technology driven systems that aim to improve the utility of healthcare delivery systems. Most have only tended to target a subset of the core architectures that drive the same.

We demonstrate in this publication the importance of unstructured data processing to achieve semi-structured maternal, neonatal and child health (MNCH) datasets curated directly from patients’ medical file/records, to support intelligent health data mining, informed policy planning and robust decision support system design.

The idea behind this statement in the introduction section of the manuscript is to present the study objectives. In my opinion this objective is not elaborately presented in the current wording of the statement. Consider rephrasing.

.......expected hypotheses on the applications of our datasets

What does this mean?

MNCH datasets would encourage the development of resources for the sub-Saharan African region

What do the authors mean by this statement? Which resources are in discussion. Consider rephrasing.

MNCH datasets would drive integrated GeoAI applications for robust spatiotemporal data analysis.

If this hypothesis is true, why has MNCH datasets failed to achieve the same all along? Consider rephrasing.

Do the authors still mean maternal, neonatal and child health by MNCH? If so, in my opinion I think the three hypothesis statements must be revised. MNCH datasets have ever been there both in developed and developing communities. The new argument which I thought the authors would bring is the utility of technology driven/AI powered MNCH datasets.

Data source, sample size and capturing procedure

Would the description presented here still not amount to person level data capture. SEE ---Ethical clearance was granted by the University of Uyo Health Research Ethics Committee (UNIUYO-HREC) – Ref. number: UU/CHS/IHREC/VOL.I/017 with the acceptance that the study did not require direct contact with patients.

While the investigators would not know the the physical person behind each hospital record. It is possible that being a local community there is bound to be a level of familiarity with each other at community level. What protocols were put in place to blind the same since not individual patient consent was sought?

Furthermore, patient’s location/address was truncated by removing street number, to avoid privacy and security implication.

Whilst this is OK from an ethics point of view, I am of the opinion that the approach will compromise the geo-mapping utilities required by the investigators. What did the investigators do make sure that mapping was still possible using information captured in this dataset after dropping off this field. Would that amount to going back to the parent records after patient profile data capture to achieve precise location establishment at field data capture?

Due to ethical reasons, we are only interested in the vicinity of the patient, hence the defined accuracy range.

1 - 9.65 metres amounts to a precise location especially in less populated communities. What is the average household to household distance in the study area?

Are sufficient details of methods and materials provided to allow replication by others?

Yes

Is the rationale for creating the dataset(s) clearly described?

Are the datasets clearly presented in a useable and accessible format?

Yes

Are the protocols appropriate and is the work technically sound?

Partly

Reviewer Expertise:

Geographic Information Systems, Health Geography or Spatial Epidemiology

Ekpenyong

Moses

University of Uyo, Nigeria

Competing interests: No competing interest

5 9 2023

Comment 1:

........ Hence, knowledge of health facilities is crucial for providing informed health planning decisions.

The statement may need to be qualified. What knowledge of health facilities are the authors referring to here?

Response 1:

We are referring to facilities offering MNCH services. statement is rephrased to "...Hence, knowledge of available MNCH facilities"

Comment 2:

.......with the urban areas holding very high concentration of patients’ population per facility.

.....as major factors mitigating access to quality healthcare

I would use technical terms like patient to provider ratio to describe the same.

Response 2:

The statement hase been rephrased to "...In urban areas, there is a notable imbalance in the patient-to-provider ratio, with a significant concentration of the patient population per healthcare facility"

Comment 3:

mitigating factors: finance (poor living condition), increased security threat, type and nature of ailment, geography of residence, race and ethnicity, gender, age, language, and disability. These factors significantly determine the degree at which medical/healthcare services – including availability, timeliness, convenience, and affordability, are utilised

What are mitigating factors?

Response 3:

Mitigating factors are factors hindering access to quality healthcare. Hence the paragraph has been rephrased as:

Oleribe et al. (2019) identified major factors hindering access to quality healthcare in sub-Saharan Africa, including financial barriers, poor governance, and limited infrastructure. In Nigeria identified hidering factors include, financial constraints due to poor living conditions, heightened security threats affecting healthcare access, the type and nature of ailments influencing healthcare choices, geographic residence impacting access, racial and ethnic disparities affecting healthcare outcomes, gender-related disparities, age-related healthcare considerations, language barriers affecting communication, and disability-related healthcare challenges. These factors significantly influence the utilization of medical and healthcare services in terms of availability, timeliness, convenience, and affordability (Babalola & Fatusi, 2009).

Comment 4:

Adoption of modern technology has however simplified healthcare services through the implementation of automated systems.

What do the authors mean by this statement? Consider rephrasing for clarity.

Response 4:

Statement rephrased as "The integration of modern technology into the health sector has simplified healthcare services"

Comment 5:

.....that defies real-time, patient-centred care.

Again this statement may require clarification. How does unstructured data defy patient-centred care?

Response 5:

The statement has been rephrased to "...defying efforts to extract meaningful insights, hinder accurate decision-making, and impede the creation of reliable healthcare solutions and strategies"

Comment 6:

.....and details of every parameter within the health system should be available in real-time.

Response 6:

The sentence has been rephrased as: "To achieve this, it's essential that technology-driven systems, aiming to enhance the utility of healthcare delivery systems, prioritize the availability of real-time location-based information and comprehensive details of the healthcare system"

Comment 7:

Response 7:

The statement is rephrased to distinguish the overall ojective and specific objectives:

To gather and compile detailed MNCH data from various sources to create a comprehensive dataset.

To develop a standardized MNCH data and structure format.

To convert unstructured healthcare data into a semi-structured format, making it suitable for analysis.

To integrate location-based information into the MNCH dataset for facilitating spatiotemporal analysis and visualization.

Comment 8:

.......expected hypotheses on the applications of our datasets

What does this mean?

Response 8:

It means the study/research hypothesis

Comment 9:

MNCH datasets would encourage the development of resources for the sub-Saharan African region

What do the authors mean by this statement? Which resources are in discussion. Consider rephrasing.

Response 9:

Statement deleted and rephrased to address the next reviewer's comment

Comment 10:

MNCH datasets would drive integrated GeoAI applications for robust spatiotemporal data analysis.

If this hypothesis is true, why has MNCH datasets failed to achieve the same all along? Consider rephrasing.

Response 10:

We agree with the reviewer's suggestion on change of hypothesis. The hypothesis are therefore restated as follows:

1) Technology driven data curation practices improves MNCH outcome predictions

2) Integration of location-based data into MNCH datasets leads to more informed policy insights and recommendations for MNCH.

3) Geo-referenced features of MNCH datasets enables real-time demographic/spatiotemporal analysis

Comment 11:

Data source, sample size and capturing procedure

Response 11:

The paragraphs in this section have been rephrased to clear this misconception as follows:

The datasets used in this study originated from patients' medical records/files retrieved from St Luke's General Hospital in Anua, Uyo, Akwa Ibom State, Nigeria, which is the healthcare facility under consideration. These records cover the period from 2014 to 2019. The process of selecting properly documented files involved physically inspecting the patients' records, which were manually stored in the hospital's file cabinets or archives. To initiate this procedure, we sought informed consent through the Chief Medical Director of the hospital to obtain the necessary data. After obtaining permission, the necessary arrangements were made to commence the exercise. It's important to note that the investigators did not have direct access to the records room or the patient archives. Only files preselected by the designated officers assigned by the Chief Medical Director of the hospital were made available for the investigators' use. Before handing over these files for the capture of attributes, the assigned officers reviewed them to ensure compliance with the primary attributes of the study, as outlined in Table 1. ...

Comment 12:

While the investigators would not know the physical person behind each hospital record. It is possible that being a local community there is bound to be a level of familiarity with each other at community level. What protocols were put in place to blind the same since not individual patient consent was sought?

Response 12:

Protocols established to maintain the confidentiality and anonymity of patients' health records and to mitigate the risk of inadvertently identifying specific individuals within the local community include:

Data De-Identification: Personal identifiers, such as names, were systematically removed or replaced with codes or pseudonyms to prevent any association between the data and specific individuals.

Comment 13:

Furthermore, patient’s location/address was truncated by removing street number, to avoid privacy and security implication.

Response 13:

We have rephrased this in the protocol. The location (Lat and Long) records are available at OSF.

Comment 14:

Due to ethical reasons, we are only interested in the vicinity of the patient, hence the defined accuracy range.

1 - 9.65 metres amounts to a precise location especially in less populated communities. What is the average household to household distance in the study area?

Response 14:

Please see above. Statement deleted. Average household to household distance not established. May be useful for future works.

10.5256/f1000research.77502.r157440

Reviewer response for version 1

Macharia

Peter M.

Competing interests: No competing interests were disclosed.

19 12 2022

2022

recommendation

approve-with-reservations

Sharing such datasets is key for research. It facilitates reproducibility, generation of new research questions to improve the health of community or develop new research approaches. More important, when these datasets have linked location details, additional insights linked to space can be harnessed. I have a few thoughts which I welcome the authors to consider:

A location map showing the approximate hospital catchment for which the datasets are available. You could also show other facilities in the vicinity, useful for context.

In addition to the map, provide general details/characteristics of the study area for researchers/potential users of the data but not familiar with Nigeria and the region.

The introduction has mainly concentrated on health care access aspects of health facilities. However, not much has been said in relation to other key attributes of the data provided. That is, the data provides attributes that spans beyond a discourse of health care access only.

ArcGIS should be given as an example of a geospatial software among others but not as the only option. Please paraphrase the sentence.

In the abstract, you state, “The curated datasets also involved the capturing of location information from the study area, to aid spatiotemporal and informed demographic analysis”. Should it not be aid in spatial and spatio-temporal analysis?

As these datasets were refer to between 2014 and 2019, and one envisioned application will be spatio-temporal analysis, a date attribute should be availed on the main database. You can truncate it monthly or quarterly in each year to align with confidentiality guidelines.

I wonder whether the tittle could be edited to Maternal, neonatal and child health (MNCH) datasets from a hospital’s catchment area in Nigeria between 2014 and 2019?

Are sufficient details of methods and materials provided to allow replication by others?

Yes

Is the rationale for creating the dataset(s) clearly described?

Yes

Are the datasets clearly presented in a useable and accessible format?

Partly

Are the protocols appropriate and is the work technically sound?

Yes

Reviewer Expertise:

Geospatial health/spatial epidemiology

Ekpenyong

Moses

University of Uyo, Nigeria

Competing interests: No competing interest

5 9 2023

Comment 1:

A location map showing the approximate hospital catchment for which the datasets are available. You could also show other facilities in the vicinity, useful for context.

Response 1:

Reviewer's comment is addressed and captured as Fig. 1.

Comment 2:

In addition to the map, provide general details/characteristics of the study area for researchers/potential users of the data but not familiar with Nigeria and the region.

Response 2:

Reviewer's comment addressed. general details/characteristics of the study area provided as follows:

St Luke’s General Hospital, Anua is one of the foremost Missionary Hospitals in the South-South and South-East Nigeria. The hospital is located along Nwaniba Road in Uyo Metropolis of Akwa Ibom State, Nigeria, West Africa. A location map showing the approximate hospital catchment for which the datasets are available is presented in Fig. 1.

Comment 3:

Response 3:

Other key attributes of the data as provided in Tables 1 and 2 and are well documented and explained in the OSF, as prescribed by the F1000Research.

Comment 4:

ArcGIS should be given as an example of a geospatial software among others but not as the only option. Please paraphrase the sentence.

Response 4:

Sentence paraphrased and enriched as follows:

Geospatial software tools for managing and visualizing MNCH datasets are discussed below: