Variation Patterns in Antenatal Care Visits: A Run Chart Analysis

Vollständiger Abstract

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The high Maternal and Infant Mortality Rates necessitate fundamental quality improvement in Antenatal Care (ANC) services at the primary level. This study retrospectively analyzed 24 consecutive months of ANC visit data from 2017 to 2018 at Puskesmas Pambusuang using a run chart to characterize temporal process variation. A descriptive analytical study was conducted using secondary data comprising 24 consecutive months of ANC visits. The data were analyzed using a run chart as a Statistical Process Control (SPC) tool to identify non-random patterns and characterize process variation. The total number of ANC visits decreased from 642 in 2017 to 611 in 2018. Run chart analysis showed no statistically significant non-random patterns for clustering (p = 0.105), trend (p = 0.201), or oscillation (p = 0.799). Ten runs about the median were identified compared with 13.0 expected runs, indicating that the observed fluctuations were consistent with common cause variation. These findings suggest that monthly ANC visit patterns were statistically stable during the observation period, with insufficient evidence of special cause variation. However, statistical stability cannot be interpreted as optimal or suboptimal service performance because the analysis was based on absolute visit counts without considering the target population of pregnant women or ANC coverage indicators. Continuous time-series monitoring integrating ANC visit data with K1, K4, and K6 coverage indicators is recommended to support comprehensive quality evaluation and early detection of meaningful changes in service utilization.