TABLE 1.

Network measures reported in this study with definitions and interpretations

Network measure	Definition	Interpretation a
Density	Ratio of the number of links present between employees vs the maximum number of possible links. The measurement is normalized between 0 and 1 to account for network size.	Measurements closer to 1 signify that there are relatively many communication links between employees, suggesting inefficient or repetitive information exchange. Measurements closer to 0 signify relatively few communication links, suggesting that information is not flowing.
Centralization	Centralization of the employee communication network is calculated from the total number of direct links each employee has divided by the maximum possible sum of differences, normalized between 0 and 1.	Measurements closer to 1 signify that many links are concentrated around a few nodes, suggesting more centralized information flow with hierarchical “command and control” decision making. Measurements closer to 0 signify little variation in the number of links each employee has, suggesting more decentralized information flow with decisions made closer to point of service.
	If every employee in the network were linked only to a single “leader” at the center, the network would look like a star (*) and centralization would be 1. In a decentralized network, the links are more dispersed.
Complexity	A composite measure to approximate interdependencies and integration. It represents the ratio of links present in all four matrices vs the maximum number of possible links (ie, employee × employee; employee × knowledge; employee × task; and employee × resource), normalized between 0 and 1.	Measurements that are closer to 0 signify that interdependency and integration are low, suggesting duplication of effort and inefficiency. Measurements closer to 1 signify that interdependency and integration are high, suggesting that error “cascades” are more likely (ie, one error leads to subsequent errors in all dependent areas).
Clustering coefficient	The average of the proportion of links between each employee and other employees to which he or she is directly linked divided by the number of links that could possibly exist between them, normalized between 0 and 1 (eg, three employees can communicate directly with each other, but in fact only two of them may do so).	The clustering coefficient measures of degree to which employees tend to cluster together in terms of communication. It gives a sense of the local characteristics of the network—how information flows among small groups of employees. An optimal level of clustering supports local information sharing and a decentralized infrastructure.
Percentage divisions tending toward silo	The percentage of an LHD’s divisions or programs with an SI ≥ 0.5. The SI is the proportion of communication links that are between two members of the same division vs communication links that are between members of different divisions.	In an information silo, communication between employees is internal and vertical within a division or program. Information silos can make overall organizational coordination and communication difficult to achieve, with a deleterious effect on performance.

Abbreviations: LHD, local health department; SI, silo index.

^aNetwork measurements must be interpreted in the context of the organization’s size and the type of work being done. Network measurements, depending on the circumstances, tend to be less advantageous both when they are very high or very low. For example, optimal performance in a small shoelace factory may be achieved with relatively few communication links between employees (low density), whereas the opposite may be true in a small research laboratory. Table 2 gives the range for each measurement in the 11 LHDS studied, which can serve as benchmarks for LHDs.