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Measures are important to healthcare outcomes. Outcome changes result from deliberate selective intervention introduction on a measure. If measures can be characterized and categorized, then the resulting schema may be generalized and utilized as a framework for uniquely identifying, packaging and comparing different interventions and probing target systems to facilitate selecting the most appropriate intervention for maximum desired outcomes. Measure characterization was accomplished with multi-axial statistical analysis and measure categorization by logical tabulation. The measure of interest is a key provider productivity index: “patient visits per hour,” while the specific intervention is “patient schedule manipulation by overbooking.” The paper aims to discuss these issues.

For statistical analysis, interrupted time series (ITS), robust-ITS and outlier detection models were applied to an 18-month data set that included patient visits per hour and intervention introduction time. A statistically significant change-point was determined, resulting in pre-intervention, transitional and post-effect segmentation. Linear regression modeling was used to analyze pre-intervention and post-effect mean change while a triangle was used to analyze the transitional state. For categorization, an “intervention moments” table was constructed from the analysis results with: time-to-effect, pre- and post-mean change magnitude and velocity; pre- and post-correlation and variance; and effect decay/doubling time. The table included transitional parameters such as transition velocity and transition footprint visualization represented as a triangle.

The intervention produced a significant change. The pre-intervention and post-effect means for patient visits per hour were statistically different (0.38, p=0.0001). The pre- and post-variance change (0.23, p=0.01) was statistically significant (variance was higher post-intervention, which was undesirable). Post-intervention correlation was higher (desirable). Decay time for the effect was calculated as 11 months post-effect. Time-to-effect was four months; mean change velocity was +0.094 visits per h/month. A transition triangular footprint was produced, yielding 0.35 visits per hr/month transition velocity. Using these results, the intervention was fully profiled and thereby categorized as an intervention moments table.

One limitation is sample size for this time series, 18 monthly cycles’ analysis. However, interventions on measures in healthcare demand short time cycles (hence necessarily yielding fewer data points) for practicality, meaningfulness and usefulness. Despite this shortcoming, the statistical processes applied such as outliers detection, t-test for mean difference, F-test for variances and modeling, all consider the small sample sizes. Seasonality, which usually affects time series, was not detected and even if present, was also considered by modeling.

Obtaining an intervention profile, made possible by multidimensional analysis, allows interventions to be uniquely classified and categorized, enabling informed, comparative and appropriate selective deployment against health measures, thus potentially contributing to outcomes optimization.

The inevitable direction for healthcare is heavy investment in measures outcomes optimization to improve: patient experience; population health; and reduce costs. Interventions are the tools that change outcomes. Creative modeling and applying novel methods for intervention analysis are necessary if healthcare is to achieve this goal. Analytical methods should categorize and rank interventions; probe the measures to improve future selection and adoption; reveal the organic systems’ strengths and shortcomings implementing the interventions for fine-tuning for better performance.

An “intervention moments table” is proposed, created from a multi-axial statistical intervention analysis for organizing, classifying and categorizing interventions. The analysis-set was expanded with additional parameters such as time-to-effect, mean change velocity and effect decay time/doubling time, including transition zone analysis, which produced a unique transitional footprint; and transition velocity. The “intervention moments” should facilitate intervention cross-comparisons, intervention selection and optimal intervention deployment for best outcomes optimization.

The current study examines the effect of changing a specific use-of-force policy coupled with de-escalation training implementation on patterns of police use of force.

An interrupted time-series analysis was used to examine changes in police use-of-force incident records gathered from a large, southwestern US metropolitan police department from 2013 to 2017 based on a TASER policy change and de-escalation training implementation mid-2015.

Results demonstrate that changes to use-of-force policy regarding one type of force (i.e. use of TASERs) coinciding with de-escalation training influence the prevalence of use-of-force incidents by increasing the reported police use-of-force incidents after the changes were implemented. This finding is somewhat consistent with prior literature but not always in the desired direction.

When police departments make adjustments to use-of-force policies and/or trainings, unintended consequences may occur. Police administrators should measure policy and training outcomes under an evidence-based policing paradigm prior to making those adjustments.

This study is the first to measure the effects of changing use-of-force policy and implementing de-escalation techniques in training on patterns of police use of force and shows that these changes can have a ripple effect across types of force used by police officers.

‘Subjective well-being comprises of people's emotional responses, domain satisfactions, and global judgements of life satisfaction’ (Diener et al., 1999). The health-related Sustainable Development Goal (SDG-3) aims to ensure ‘good health and well-being’ for all, over the globe. The World Happiness Report (2022) reported a highly significant relation between the SDG-3 and the subjective well-being scores and hence for the improvement of citizen well-being suggested for a holistic approach to economic development. The present chapter examines the impact of global economic crisis 2008–09 on the subjective well-being using time series data for six selected countries for the time period 2004–2019. Considering the crisis as an ‘intervention’, this chapter performs interrupted time series analysis for single- and multiple-group (country) comparisons. The single group analysis finds that in the immediate year of crisis, there appeared to be significant decrease in subjective well-being, followed by a significant decrease in the annual trend subjective well-being relative to pre-crisis for most of the countries. In case of multiple group analysis, the regression results reveal that initial mean level difference between any country and remaining countries was significant for most of the countries. The difference in the subjective well-being trend between a particular country and remaining countries after initiation of the crisis compared to pre-crisis period has appeared to be significantly negative for all the countries considered in the analysis.

The purpose of this paper is to evaluate the impact of Queensland Police Service's version of COMPSTAT, known as “Operational Performance Reviews” (OPRs), on reported crime.

The paper employed interrupted time series analytic techniques to examine the impact of OPRs on various categories of reported crime in Queensland. The analyses assessed the extent to which OPRs were associated with crime reductions across the 29 police districts in Queensland.

The introduction of OPRs was found to be associated with a significant decrease in the total number of reported offences in Queensland. OPRs were found to have their strongest effect on reported unlawful entries into dwellings and unlawful entries into other properties. The overall finding was that OPRs appeared to be a cost‐effective approach to controlling crime, resulting in an overall saving of AUD$1,162,175.

The current study suggests that COMPSTAT generally – and Queensland's OPRs in particular – can be an effective police management mechanism, which results in observable reductions in reported crimes. Moreover, OPRs appear to be most effective at reducing property‐related crime. Modifications of this performance management process may help to generalize its effects to other types of crime.

This study was the first to provide a systematic examination of the impact of OPRs on a relatively large number of offence categories.

Interrupted time series methodology (ARIMA) is utilized to assess the effectiveness of a police initiated stolen vehicle investigative task force. Police from four municipalities in two New Jersey counties created a multi‐jurisdictional task force in response to growing incidents of auto thefts in downtown (urban) areas. The task force uses a unique investigative approach where they focus their investigations on the vehicle recovery site. Police theorize that vehicle thieves abandon their stolen vehicles close to home or near the location of their next target.

The purpose of this paper is to estimate the impact of the 2008 and 2020 economic crises on employment in Spain.

The authors perform a counterfactual analysis, combining intervention (interrupted time series) analysis and conditional forecasting to estimate a “crisis-free” scenario. These counterfactual estimates are used as a synthetic control, to be compared with the observed values of the main variables of the Spanish Labor Force Survey (EPA).

The authors measure the effect on Spanish employment of the 2008 recession and the ongoing COVID/Ukraine crisis and the speed of recovery, which yields a rigorous dating for the beginning and end of the crises studied. Finally, the authors provide estimates about which part of the employed and unemployed people was in furlough (ERTE) based on microdata provided by the Spanish Institute of Statistics.

To the best of the authors’ knowledge, there are no counterfactual studies covering all the basic variables in EPA and no estimates for the effect of ERTEs on the basic employment variables. Finally, the authors combine well-known intervention and forecasting techniques into an integrated framework to assess the effects of both, past and ongoing crises.

This study aims to examine the effect of the 9/11 terrorist attacks on suicide terrorism in different regions of the world and changes in the trends in suicide terrorism according to regions before and after 9/11.

Using the data obtained from the Global Terrorism Database from 1981 to 2019, the descriptive statistics were computed first and then, independent samples t-tests were run to compare the monthly mean percentage of suicide-terrorism incidents that occurred in each region between the pre-9/11 and the post-9/11 periods. Finally, to statistically assess the effect of the 9/11 attacks and changes in the trends for the dependent variables over time, monthly interrupted time-series analyzes were conducted.

The results of monthly interrupted time series analyzes showed that after the 9/11 attacks, the trends for suicide-terrorism rates decreased significantly in three regions including South Asia, the Middle East and North Africa and Europe, while the trend for suicide-terrorism rates increased significantly in Sub-Saharan Africa. However, no statistically significant changes in the trends in suicide-terrorism rates occurred in three regions including North America, East Asia and Central Asia and Southeast Asia before 9/11, during November 2001 or after 9/11.

This study indicates the critical importance of the 9/11 terrorist attacks in suicide terrorism and its impact on these events in different regions of the world. The research also provides some recommendations concerning the effectiveness of defensive and offensive counterterrorism policies against suicide terrorism.

This paper adds to a growing body of research which explores the relationship between aggressive police strategies and serious crime. For one month, police enforced disorder crime in a small section of one community. An interrupted time series analysis was utilized to evaluate the effects of this intervention on robbery and aggravated burglary in a target area and a control area. The strategy was found to be unrelated to levels of aggravated burglary and robbery in the target area. There was no spatial displacement of crime. Explanations for the findings are offered.

In this chapter, we identify three distinct transformational performance improvement (TPI) approaches commonly used to redesign work processes in health care organizations. We describe the unique components or tools that each approach uses to improve the delivery of health services. We also summarize what is empirically known about the effectiveness of each TPI approach according to systematic reviews and recent studies published in the peer-reviewed literature. Based on examination of this research, we discuss what knowledge is still needed to strengthen the evidence for whole system transformation. This involves the use of conceptual frameworks to assess and guide implementation efforts, and facilitators and barriers to change as revealed in a recent evaluation of one major initiative, the Lean Enterprise Transformation (LET) at the Veterans Health Administration. The analysis suggests ways in which TPI facilitators can be developed and barriers reduced to improve the effectiveness and sustainability of quality initiatives. Finally, we discuss appropriate study designs to evaluate TPI interventions that may strengthen the evidence for their effectiveness in real world practice settings.

This paper analyzes the regulatory effects on homeownership and rental prices in Spain in the years between 1977m1 and 2019m5, taking into account the economic crisis. It also studies the causal relationship of the prices of private housing (owned and rented).

Interrupted time series analysis has been used to analyze the legislative impact on the construction sector. Also, the Box–Cox transformation has been used to carry out the above analysis. Finally, Granger's test has made possible to determine the causal relationship between the price series.

The results obtained in this work show the partial positive effects of legislative instrumentalization, which in general result in decreasing trends of prices in both types of housing use. Likewise, the causal relationship between the prices of owned and rented housing shows that the latter do not provide any incentive with respect to the former.

The social implications in this work could be important since this paper analyzes the effects of (de)regulation on housing prices in Spain, and the policy implications could help to control the speculation in housing.

The results obtained in this work could be relevant, both from the point of view of future housing policies and of the agents involved and that of society in general. In addition, this work could contribute to fill the existing gap in this field in Spain, given the scarce literature that raises the objective, using quantitative methodologies.