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

Abstract

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.

Pharmacotherapy for Infertility in Ghana: A Prospective Study on Prescription Patterns and Treatment Outcomes among Women undergoing Fertility Treatment

“…However, according to the Ghana Health Services report, the fertility rate within the metropolis was 2.2, and the general fertility rate of 59.2 births per 1000 women aged between 15 and 49 years. 30 , 31 Also, although a sufficiently big sample size was used in this study, it is not large enough to conclude for all persons in diverse geographical locations. Therefore, to generalize the findings of this study, it is recommended that larger sample sizes in randomized control trials may be needed.…”

Quality of routine health facility data for monitoring maternal, newborn and child health indicators: A desk review of DHIS2 data in Lumbini Province, Nepal

“…The introduction of new data quality review interventions, including the establishment of data management committees, the rollout of DHIS2 at the facility level, and relevant knowledge transfer and data monitoring activities in the reference year might also have contributed to inconsistencies in reporting between the reference year and the previous three fiscal years combined. Although the studies assessing consistency over time are limited, few studies in other countries also reported some data quality issue while analyzing trend over the years [ 39 , 40 ]. Differences in values are obvious over period of time; however, if the differences are so large, it usually suggests data quality issue for further scrutiny.…”

“…These findings are better while comparing the result with other similar studies assessing outliers [ 42 ]. In contrast, no districts had reported ≥5% monthly values with moderate or extreme outliers in a study conducted in Ruwanda [ 43 ] and Ghana [ 40 ].…”

Determinants of COVID-19 data quality in the District Health Information Management System version 2 in Ahafo Region, Ghana

“…Three data collation sheets were used to collect the data on completeness, accuracy, and timeliness in all 23 health facilities over a retrospective 34-month starting from March 2020 to December 2022. These reviews were based on previous studies (5,6,11,15,23,25). The first collation sheet examined the completeness of COVID-19 data in the DHIMS-2 to see whether all data elements were filled.…”

“…The directorate was automatically hooked onto the DHIMS-2 and started recording high COVID-19 cases after the first case was registered in March 2020, the same month the country recorded its first incident (21). Data documentation and reporting of COVID-19 diseases then took center stage, although numerous studies have shown that data from Ghanaian health facilities are of poor quality in terms of accuracy and timeliness (18,22,23). This highlights the need to assess COVID-19 data quality using the three dimensions of data quality at different levels of the healthcare system.…”