The Quality of Newborn Data: Assessment of Data Management and Reporting System

Achampong, Emmanuel Kusi; Adzakpah, Godwin; Boadu, Richard Okyere; Lasim, Obed

doi:10.11591/ijphs.v7i3.14386

Cited by 3 publications

(2 citation statements)

References 11 publications

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“…This suggests that the MCH data transferred from the register to the monthly report forms, register to DHIS2, and forms to DHIS2 were accurate. This finding is similar to a study in Ghana, where newborn health data transferred from facilities registers to the reporting forms and DHIS2 database were reported accurate [25].…”

Section: Discussionsupporting

confidence: 90%

Maternal and child health data quality in health care facilities at the Cape Coast Metropolis, Ghana

Lasim

Ansah

Apaak

2022

BMC Health Serv Res

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Background The demand for quality maternal and child health (MCH) data is critical for tracking progress towards attainment of the Sustainable Development Goal 3. However, MCH cannot be adequately monitored where health data are inaccurate, incomplete, untimely, or inconsistent. Thus, this study assessed the level of MCH data quality. Method A facility-based cross-sectional study design was adopted, including a review of MCH service records. It was a stand-alone study involving 13 healthcare facilities of different levels that provided MCH services in the Cape Coast Metropolis. Data quality was assessed using the dimensions of accuracy, timeliness, completeness, and consistency. Health facilities registers were counted, collated, and compared with data on aggregate monthly forms, and a web-based data collation and reporting system, District Health Information System (DHIS2). The aggregate monthly forms were also compared with data in the DHIS2. Eight MCH variables were selected to assess data accuracy and consistency and two monthly reports were used to assess completeness and timeliness. Percentages and verification factor were estimated in the SPSS version 22 package. Results Data accuracy were recorded between the data sources: Registers and Forms, 102.1% (95% CI = 97.5%—106.7%); Registers and DHIS2, 102.4% (95% CI = 94.4%—110.4%); and Forms and DHIS2, 100.1% (95% CI = 96.4%—103.9%). Across the eight MCH variables, data were 93.2% (95% CI = 82.9%—103.5%) complete in Registers, 91.0% (95% CI = 79.5%—102.5%) in the Forms, and 94.9% (95% CI = 89.9%—99.9%) in DHIS2 database. On the average, 87.2% (95% CI = 80.5%—93.9%) of the facilities submitted their Monthly Midwife’s Returns reports on time, and Monthly Vaccination Report was 94% (95% CI = 89.3%—97.3%). The overall average data consistency was 93% (95% CI = 84%—102%). Conclusion Given the WHO standard for data quality, the level of MCH data quality in the health care facilities at the Cape Coast Metropolis, available through the DHIS2 is complete, reported on timely manner, consistent, and reflect accurately what exist in facility’s source document. Although there is evidence that data quality is good, there is still room for improvement in the quality of the data.

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Section: Discussionsupporting

confidence: 90%

Maternal and child health data quality in health care facilities at the Cape Coast Metropolis, Ghana

Lasim

Ansah

Apaak

2022

BMC Health Serv Res

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“…A retained placenta is considered to have occurred if the placenta has not been delivered within 30 minutes after the baby is born [26]. Another sign is that the uterus is well contracted, bleeding is immediate, or due to excessive traction the umbilical cord breaks which prompts management of bleeding [15], [27]. Furthermore, if the uterus contracts but the height of the uterine fundus does not decrease and part of the placental membrane is incomplete, then bleeding may occur immediately and part of the placenta will be left behind [1].…”

Section: Introductionmentioning

confidence: 99%

Risk identification for early warning of bleeding among mothers during childbirth

Sulastri

Siregar²,

Hasanuddin³

et al. 2023

IJPHS

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The study was conducted using the observational method. It assessed blood loss using a calibrated measuring cup every 15 minutes in the first hour after delivery and every 30 minutes for the next hour. The results of the analysis showed that the modelling of early warning identification in laboring mothers is influenced by the age and weight of the mother, which is at a significance of 10%. A major warning sign is an increase in blood pressure rate in the third stage of labor which begins 15 minutes from the start of bleeding, and is categorized as dangerous when the blood volume reaches 600 mL. Therefore, every mother in labor must be monitored regularly and continuously and early warning signs must be taken into account. There is no safe time in cases of blood loss. It is very important to identify the risks in a timely manner to identify the source of bleeding so that it can be handled quickly and appropriately in order to prevent complications and maternal death due to postpartum haemorrhage (PPH). Implication, effective and optimal identification in clinical practice should be evidence-based in order to better determine whether or not intervention is needed to prevent blood loss after childbirth.

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Data completeness and consistency in individual medical records of institutional births: retrospective crossectional study from Northwest Ethiopia, 2022

Taye,

Gezie,

Atnafu

et al. 2023

BMC Health Serv Res

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Background Ensuring the data quality of Individual Medical Records becomes a crucial strategy in mitigating maternal and newborn morbidity and mortality during and around childbirth. However, previous research in Ethiopia primarily focused on studying data quality of institutional birth at the facility level, overlooking the data quality within Individual Medical Records. This study examined the data completeness and consistency within Individual Medical Records of the institutional birth service and associated factors. Methods An institution-based retrospective cross-sectional study was conducted in two districts of Northwest Ethiopia. Data were obtained by reviewing three sets of Individual Medical Records of 651 women: the delivery register, Integrated Individual Folder, and integrated card. The proportions of completeness and consistency were computed. A multilevel binary logistic regression was used to identify factors of completeness and consistency. An odds ratio with a 95% confidence interval was used to assess the level of significance. Results Overall, 74.0% of women’s Individual Medical Records demonstrated good data completeness ( > = 70%), 95%CI (70.5, 77.3), while 26% exhibited good consistency, 95%CI (22.9, 29.7). The presence of trained providers in data quality (AOR = 2.9, 95%CI: (1.5, 5.7)) and supportive supervision (AOR = 11.5, 95%CI: (4.8, 27.2)) were found to be associated with completeness. Health facilities’ practice of root cause analysis on data quality gaps (AOR = 8.7, 9%CI: (1.5, 50.9)) was statistically significantly associated with the consistency. Conclusions Most medical records were found to have good completeness, but nearly only a quarter of them found to contain consistent data. Completeness and consistency varied on the type of medical record. Health facility’s root cause analysis of data quality gaps, the presence of trained providers in data quality, and supportive supervision from higher officials were identified as factors affecting data quality in institutional birth service. These results emphasize the importance of focused efforts to enhance data completeness and consistency within Individual Medical Records, particularly through consideration of Individual Medical Records in future provider training, supervision, and the implementation of root cause analysis practices.

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The Quality of Newborn Data: Assessment of Data Management and Reporting System

Cited by 3 publications

References 11 publications

Maternal and child health data quality in health care facilities at the Cape Coast Metropolis, Ghana

Maternal and child health data quality in health care facilities at the Cape Coast Metropolis, Ghana

Risk identification for early warning of bleeding among mothers during childbirth

Data completeness and consistency in individual medical records of institutional births: retrospective crossectional study from Northwest Ethiopia, 2022

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