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Related Concept Videos

Introduction to Statistical Process Control01:15

Introduction to Statistical Process Control

Statistical Process Control (SPC) is a method used to monitor and control quality within processes, particularly in manufacturing and service delivery, by employing statistical methods. SPC aims to distinguish between natural (common cause) variation and variation due to specific changes or events (special cause), allowing for timely improvements and sustained quality. The control chart, a pivotal tool in SPC, visually displays data over time alongside a central line of upper and lower control...
The R Chart01:02

The R Chart

In statistical process control, control charts, particularly R charts, are instrumental in monitoring process variations and identifying non-random patterns that run charts might miss. R charts track the variability within process subgroups, which is crucial when standard deviation use is impractical or unknown process variations exist.
R charts are pivotal for pinpointing shifts in process variability. Stability is indicated when all data points remain within the defined upper and lower...
The X̄ Chart00:58

The X̄ Chart

The  x̄ chart is a statistical tool for monitoring the means in a process.
The x̄ chart, often known as the individual control chart, is a crucial tool in statistical process control. It is designed to monitor process behavior and performance over time and is widely used in various industries to ensure that processes are operating at their optimum capacity and within specified limits.
A x̄ chart is constructed by plotting individual measurements of a quality characteristic in the order in which...
Interpreting Run Charts01:25

Interpreting Run Charts

Run charts, essentially line graphs plotted over time, serve as fundamental yet effective tools for process analysis. They chronicle data sequentially, facilitating the identification of trends, shifts, or cyclical movements. This graphical representation is instrumental in determining whether a process is stable or exhibits signs of potential instability indicative of special cause variation. In the healthcare domain, run charts depict infection rates over time, enabling hospitals to monitor...
Interpreting X̄ Charts01:13

Interpreting X̄ Charts

Interpreting x̄ charts, a type of control chart used in statistical process control helps monitor the variation in processes over time. The x̄ chart is based on the sample mean and allows for monitoring variations in the process mean over time. These charts are pivotal for quality assurance in manufacturing and other sectors.
An x̄ chart plots the values of individual measurements over time against control limits calculated from historical data. The central line represents the process mean,...
Run Charts01:12

Run Charts

Run charts serve as an essential instrument for visualizing the performance of various processes over time, enabling the identification of trends and patterns crucial for quality improvement. These charts map out a series of data points chronologically, offering insights into the stability and efficiency of a process. A run chart's creation involves plotting data points on a graph, with the time intervals on the horizontal axis and the specific measurements on the vertical axis. For example,...

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Articles linked to this work by shared authors, journal, and citation graph.

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Same author

[Birth-weight curves].

Revista de salud publica (Bogota, Colombia)·2007
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Related Experiment Video

Updated: Jun 20, 2026

Modeling Ascending Vaginal Infection, Preterm Birth, and Neonatal Morbidity in Mice
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Modeling Ascending Vaginal Infection, Preterm Birth, and Neonatal Morbidity in Mice

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[Statistical process control charts in perinatal mortality surveillance].

Nora E Montoya-Restrepo1, Juan C Correa-Morales

  • 1SUSALUD, Medellín, Colombia. noramore@susalud.com.co

Revista De Salud Publica (Bogota, Colombia)
|September 2, 2009
PubMed
Summary

Statistical process control (SPC) charts effectively monitor perinatal mortality rates. These charts help quickly identify changes in healthcare quality for mothers and newborns, enabling targeted interventions.

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Published on: October 25, 2015

Area of Science:

  • Epidemiology
  • Public Health Surveillance
  • Statistical Quality Control

Context:

  • Perinatal mortality surveillance is crucial for assessing maternal and infant health outcomes.
  • Traditional surveillance methods may not always provide timely insights into quality variations.
  • The study utilizes data from SUSALUD spanning January 2004 to December 2007, encompassing 51,840 births.

Purpose:

  • To apply statistical process control (SPC) charts as an epidemiological indicator for perinatal mortality surveillance.
  • To develop and present two distinct SPC charts for monitoring perinatal mortality trends.
  • To evaluate the utility of SPC charts in detecting changes in healthcare quality.

Summary:

  • Two SPC control charts were constructed using perinatal mortality data from 51,840 births.
  • The first chart monitored the monthly proportion of cases, identifying an average of five cases per thousand births (p=0.005).
  • The second chart employed logits of case percentages for an alternative monitoring approach.

Impact:

  • SPC charts provide a dynamic tool for the rapid detection of shifts in perinatal mortality rates.
  • These charts facilitate the evaluation of healthcare service quality for mothers and newborns.
  • Implementation of SPC charts can inform and guide the programming of specific public health interventions.