# Empirical Evaluation of the Possible Contribution of Group Practice of the Transcendental Meditation and TM-Sidhi Program to Reduction in Drug-Related Mortality

^{*}

## Abstract

**:**

^{®}approach that may possibly help reduce trends in drug-related fatalities by mitigating what WHO refers to as an “epidemic of stress” in society that helps fuel drug misuse and other negative public health trends. This approach involves providing support in public and private sector public health initiatives for individual and group practice of a subjective, evidence-based meditation procedure suitable for those of all educational, cultural, and religious backgrounds: the Transcendental Meditation

^{®}(TM

^{®}) technique and its advanced aspect, the TM-Sidhi

^{®}program. Materials and Methods: Segmented-trend regression analysis of monthly CDC data on U.S. drug-related fatality rates (dfr) from a prospective social experiment (2002–2016) was used to replicate and extend prior peer-reviewed research. Results: As hypothesized, (1) practice of the TM and TM-Sidhi program by a group of theoretically predicted size (√1% of the U.S. population) was associated with a statistically and practically significant reduction in dfr trend during the five-year “demonstration period” of the quasi-experiment; and (2) monthly dfr trend subsequently increased during the five-year follow-up period when the group fell below the required size (both p’s < 0.0001). The estimated total percent decrease in dfr during the demonstration period was 35.5%, calculated relative to the baseline mean. This decline was followed by total dfr increases of 11.8% and 47.4% relative to the demonstration-period mean during the two phases of the follow-up period. Conclusion: Existing evidence warrants implementation and further evaluation of this approach in U.S. public health initiatives.

## 1. Introduction

#### 1.1. The Epidemic of U.S. Drug-Related Mortality

#### 1.2. U.S. Epidemic of Stress

#### 1.3. Structure of the Paper

## 2. Consciousness-Based Approach to Decreasing Drug-Related Mortality

#### 2.1. Transcendental Meditation Technique

#### 2.2. Research on Health-Related Behavioral Effects of TM Practice

#### 2.3. Reduction of Societal Stress and Its Effects

#### 2.4. Principles from Ancient and Contemporary Theorists

^{®}(MVST

^{®})”.

#### 2.5. The Concept of Collective Consciousness: Historical Precedents and Contemporary Examples

#### 2.6. Prior Empirical Research on the Current Social Experiment

^{2}because unsquared measures are said to better indicate the relative magnitude of effects across variables [67]. For details, see file S1_Appendix_A in the online Supplemental Files for this article at the Open Science Framework (OSF) repository (https://osf.io/vbkfc/, accessed on 15 January 2023).

## 3. Other Previous Empirical Research on the Maharishi Effect

#### 3.1. Research on Public Health Related Indicators: City Level

#### 3.2. Research on Public Health Related Indicators: Province or State Level

#### 3.3. Research on Public Health Related Indicators: National Level

## 4. Methods

#### 4.1. Data Definitions and Sources

#### 4.2. Interrupted Time Series (ITS) Research Design

#### 4.3. Prospective Social Experiment

#### 4.4. Baseline, Demonstration, and Follow-Up Periods

#### 4.5. Testable Hypotheses Examined in the Current Study

_{1}) is calculated by subtracting the counterfactual predicted value of dfr at the end of the demonstration period from the corresponding predicted (or fitted) value of dfr based on the full regression model. Thus, equivalently, Hypothesis 1 can be stated more concisely as predicting that the TE for the demonstration period should have negative sign (TE

_{1}< 0).

_{2}and TE

_{3}) of the study are defined analogously to TE

_{1}. Thus, Hypothesis 2 states that TE

_{2}and TE

_{3}should have positive sign.

#### 4.6. Time Series Plots of Data

#### 4.7. Segmented-Trend Regression Model

_{T}= β

_{0}+ β

_{1}Y

_{T−}

_{1}+ β

_{2}T + β

_{3}I

_{1T}+ β

_{4}I

_{1T}(T − 60) + β

_{5}I

_{2T}+ β

_{6}I

_{2T}(T − 120) + β

_{7}I

_{3T}+ β

_{8}I

_{3T}(T − 156)

+ seasonal component + ε

_{T}, T = 0, 1, 2,…, 179

_{T}is the monthly drug-related fatality rate dfr in month T. β

_{0}is the regression constant term. Y

_{T}

_{−1}is the value of dfr in the previous month (lagged dependent variable) with regression coefficient β

_{1}. T is a monthly time counter. β

_{2}is the baseline (phase1) time trend slope. β

_{3}is the coefficient for the level-shift indicator variable I

_{1T}, which is a binary indicator variable equal to zero prior to January 2007 and equal to 1 thereafter. The coefficient β

_{4}for the interaction term I

_{1T}(T − 60) gives the change in trend slope from phase1 to phase2. The interaction defines a monthly time counter that takes the values 1, 2, 3,…, 119 starting February 2007 and is zero prior to that month. Because the time counter starts at T = 0, the number 60 in the interaction term represents the 61st month of the study, January 2007.

_{6}quantifies the change in slope from the phase2 to phase3 trend that begins in January 2012. β

_{5}is the regression coefficient for I

_{2T}which is a binary indicator variable that equals zero prior to January 2012 and equals 1 thereafter. The time counter for the phase3 trend segment is given by the interaction term I

_{2T}(T − 120) = 1, 2, 3,…, 59 beginning January 2012 and equals zero before that month.

_{7}is the coefficient for the binary level-shift indicator I

_{3T}, which equals 1 beginning January 2015 and is zero otherwise. β

_{8}gives the change in slope from the phase3 to phase4 trend. The associated interaction term defines a monthly time counter for this segment, where the interaction = 1, 2, 3,…, 23 starting January 2015 and is zero prior to that month.

_{2}is the phase1 slope, the phase2 slope is β

_{2}+ β

_{4}, the phase3 trend slope is β

_{2}+ β

_{4}+ β

_{6}, and the phase4 trend slope is β

_{2}+ β

_{4}+ β

_{6}+ β

_{8}. The change in trend slope for phase4 relative to the phase2 demonstration trend is β

_{6}+ β

_{8}.

_{k}(not shown in Equation (1)) where k is the month number. For each different month k, the value of the seasonal coefficient S

_{k}shifts the intercept in Equation (1) up or down. In estimating Equation (1), the seasonal indicator for December was omitted to avoid exact linear dependence.

#### 4.8. Model Estimation

^{2/9}, where N is the number of observations [98]. Sensitivity analysis indicated that the conclusions regarding Hypotheses 1 and 2 of the current study are not sensitive to the selected bandwidth (see Section 5.5).

## 5. Results

#### 5.1. Estimated Regression Model

_{1}indicates a significant effect of the previous month’s value of dfr on that for the current month. The seasonal variation of dfr suggested in Figure 2 and Figure 3 was confirmed by the joint significance of the seasonal regression coefficients: (F(11, 159) = 22.92, p < 0.0001).

_{4}). The January 2007 level shift in dfr (β

_{3}) is also significant with negative sign. These two coefficient estimates imply a reduction in the regression predicted value of dfr relative to the baseline trend and thus are consistent with Hypothesis 1. Likewise, the significant positive estimate for the change in trend from phase2 to phase3 is consistent with Hypothesis 2, as is the positive change in phase4 trend (see Section 5.4 for formal tests of Hypotheses 1 and 2). Because there are no intercept changes for the phase3 and phase4 trend segments, the evaluation of Hypothesis 2 for each follow-up segment is based only on changes in trend slope.

_{4}, β

_{6}, and β

_{8}) quantify the initial, short-run changes in dfr trend slopes associated with phase2, phase3, and phase4 of the social experiment, respectively. β

_{3}is an estimate of the short-run (SR) phase2 level change. Due to the significant lagged dependent variable, Equation (1) is a dynamic regression model (stochastic difference equation) that has both SR coefficients and, if certain stability conditions are satisfied, corresponding long-run (LR) coefficient estimates. As discussed in Section 5.3, LR impacts of the MIU group on dfr are derived from the coefficients of the LR equilibrium (or “steady state”) solution of Equation (1). Formal tests of Hypotheses 1 and 2 are presented in Section 5.4 after operationalizing these hypotheses in terms of the estimated LR coefficient values.

_{5}) in Equation (1) was small and did not approach significance (p = 0.26). It was dropped from the model reported in Table 1 in order to improve (lower) the Akaike information criterion (AICc) for evaluation of model specification [100]. Dropping β

_{5}also improved the Bayesian information criterion (BIC) [100,101]. Both criteria are designed to balance the competing objectives of model parsimony and improved model fit. The AICc version of AIC is recommended for use in both large and small data samples [100].

_{7}) also improved both the AICc and BIC, as did adding three phase4 binary (0/1) outlier indicator variables to Equation (1) (November 2015, December 2015, and January 2016). The estimated model in Table 1 globally minimized both the AICc (118.787) and BIC (182.586) relative to the model given by simply adding these three outlier components to the full equation given in Equation (1) (AICc = 121.772, BIC = 190.661) as well as relative to other alternative models with various combinations of level shifts and outliers.

LM test for no serial correlation ^{1}: Lags 1–2: F(2, 157) = 2.925 (p = 0.057) Lags 1–7: F(7, 152) = 1.409 (p = 0.205) | LM test for no heteroscedasticity ^{2}: F(22, 154) = 1.423 (p = 0.112) |

LM test for normality ^{3}: χ ^{2} = 2.776 (p = 0.250) | LM test for no ARCH ^{4}: Lags 1–7: F(7, 166) = 1.286 (p = 0.260) |

HML test for stationarity ^{5}: z = −0.942 (p = 0.70) | KPSS test for stationarity ^{6}: test statistic = 0.0518 (p ≤ 0.88) |

#### 5.2. Diagnostic Tests

^{2}(1) = 0.697, p = 0.404). Thus, the C-H test indicates that the HAC adjustment of SEs for the presence of autocorrelated errors up to lag 4 (Newey-West bandwidth q = 4) by PcGive 15 was adequate to correct SEs and t-statistics for the regression coefficients. The C-H test was calculated using Stata 16 add-in module actest [105].

#### 5.3. Long-Run Estimates of Regression Coefficients

_{1}for the lagged dependent variable indicates the presence of first-order autoregressive dynamics of dfr. If (1 − β

_{1}) ≠ 0 and β

_{1}is less than 1.0 in absolute value, then these dynamics imply a gradual, exponential adjustment of each coefficient from its short-run value to its equilibrium (LR) value.

_{1}) = 0 (p < 0.001) in favor of the alternative that |β

_{1}| < 1.0 [98,106]. This indicates that the estimated model in Equation (1) is dynamically stable. Thus, each of the LR changes in trend slope will converge to their mathematically expected equilibrium (or “steady-state”) values.

_{1}is satisfied, the LR equilibrium value for the change in phase2 trend is given by b

_{4}= β

_{4}

**/**(1 − β

_{1}) [98]. Likewise, the corresponding LR multiplier values (as well as other coefficients in Table 1) are obtained by multiplying each SR coefficient value in Table 1 by 1/(1 − β

_{1}).

_{1}) implies an exponential decay over time in the absolute value of the monthly impact on dfr as SR coefficients approach their LR value. In the current study, LR regression coefficients will be larger (in absolute value) than the immediate impacts shown in Table 1 by a factor of 1/(1 − β

_{1}) = 1.633. After the onset of the social experiment, approximately 61.2%, 85.0%, 95.2%, and 97.7% of the cumulative increase in the absolute value of the SR coefficients will be completed within one, two, three, and four months, respectively.

#### 5.4. Tests of Research Hypotheses

_{1}< 0.

_{1}) is operationalized in terms of a linear combination of LR regression coefficients for the phase2 trend segment [68,69,90,92]. TE

_{1}is obtained by subtracting the predicted value for dfr based on the counterfactual LR equation (as evaluated at the end of phase2, December 2011) from the corresponding predicted value for dfr based on the LR coefficients of the segmented-trend regression equation for the observed data (see file S2_Appendix_B in the online Supplemental Material posted at the permanent online Open Science Framework repository https://osf.io/vbkfc/). After substituting the LR coefficient values b

_{3}and b

_{4}for their SR equivalents β

_{3}and β

_{4}, the subtraction of counterfactual from observed regression predicted values yields the following expression for TE

_{1}:

_{1}= b

_{3}I

_{1T}+ b

_{4}I

_{1T}(T − 60).

_{1}is calculated by evaluating this expression at T = 119 (December 2011) and setting I

_{1T}= 1:

_{1}= b

_{3}+ b

_{4}(119 − 60) = b

_{3}+ b

_{4}(59).

_{1}is negative and statistically significant, TE

_{1}= −3.167, z = −5.69, p < 1 × 10

^{−9}with 95% CI [−4.257, −2.076].

_{1}relative to the mean baseline value of dfr (8.930 fatalities per million population). This raw effect size indicates a reduction of 35.5% (or 7.1% annually, on average) during the five years of phase2 compared to the five-year mean monthly baseline rate. An additional measure of practical significance is the standardized effect size for the estimate of the LR change in trend slope during phase2: f = −0.703, a large effect.

_{1}is also indicated by TE

_{1}measured in units of the baseline SD of dfr (SD = 1.150): TE

_{1}= −2.754 SDs (or −0.551 SDs per year), a sizeable effect. Thus, in sum, TE

_{1}is negative, consistent with Hypothesis 1, and both practically and statistically significant.

_{2}. TE

_{2}quantifies the difference between the LR predicted value of dfr at the end of phase3 (December 2014) and the corresponding counterfactual predicted value of dfr. Hypothesis 2 predicts that TE

_{2}should have positive sign. The counterfactual predicted value is based on the extrapolation of the demonstration-period trend through phase3. TE

_{2}is given by evaluating the following expression at T = 155:

_{5}I

_{2T}+ b

_{6}I

_{2T}(T − 120)

_{2T}= 1, and setting b

_{5}equal to zero, TE

_{2}is given by the following:

_{2}= b

_{6}(155 − 120) = b

_{6}(35).

_{2}is positive and statistically significant: TE

_{2}= 1.286, z = 4.49, p < 1 × 10

^{−7}with 95% CI [0.746, 1.900]. Relative to mean dfr during the demonstration period (10.853), TE

_{2}represents an 11.8% increase in dfr (3.9% annually). In units of the baseline SD of dfr, TE

_{2}= 1.182 SDs or 0.372 SDs per year. Additionally, the standardized effect size for the LR estimate of the increase in trend for phase3 relative to the phase2 trend is f = 0.393, a medium effect. Thus, TE

_{2}is both statistically and practically significant, supporting Hypothesis 2.

_{3}= (b

_{6}+ b

_{8})(23). Hypothesis 2 also predicts that TE

_{3}, like TE

_{2}, should have positive sign. Consistent with Hypothesis 2, the value of TE

_{3}is positive and statistically significant: TE

_{3}= 5.141, z = 20.10, p < 1 × 10

^{−46}with 95% CI [4.639, 5.642]. Relative to the dfr mean during the demonstration period, TE

_{3}is a 47.4% increase in dfr (23.7% annually). In units of the baseline SD of dfr, TE

_{3}= 4.470 SDs or 2.235 SDs per year. Additionally, the standardized effect size for the LR estimate of the increase in trend for phase4 relative to the phase2 trend is also large: f = 1.594. Thus, in sum, both TE

_{3}and TE

_{2}are practically and statistically significant, further supporting Hypothesis 2.

_{1}) as well as the phase3 (TE

_{2}) and phase4 (TE

_{3}) follow-up subperiods of the 15-year quasi-experiment.

#### 5.5. Sensitivity Analyses

## 6. Discussion

#### 6.1. Summary of Empirical Results

_{1}) indicates that the predicted value of dfr at the end of the five-year demonstration period was less than the predicted value of dfr based on continuation of the baseline trend (p < 1 × 10

^{−9}), thus supporting Hypothesis 1. This finding reflects the combined effect of a significant reduction in both dfr level (intercept) and dfr trend slope relative to the baseline trend.

^{−7}) as well as the second follow-up subperiod (2015–2016) (p < 1 × 10

^{−46}). The positive sign of the treatment effect for both subperiods (TE

_{2}and TE

_{3}) indicates that the regression predicted (or fitted) value of dfr at the end of each subperiod was higher than the counterfactual predicted value based on projection of the demonstration-period trend. Equivalently, because there was no significant change in dfr level (intercept) during either subperiod, the positive treatment effect in each case reflects a significant increase in monthly dfr trend slope relative to the five-year demonstration-period trend slope.

#### 6.2. Alternative Possible Explanations for the Empirical Results

#### 6.3. Possible Mechanism of Action

#### 6.4. Possible Causal Interpretation

## 7. Conclusions

## Supplementary Materials

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Trademarks

## Conflicts of Interest

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**Figure 1.**Monthly mean number of individuals practicing Transcendental Meditation and the advanced TM-Sidhi program together in a group at Maharishi International University, Fairfield, Iowa, 2002–2016.

**Figure 3.**Actual monthly drug-related fatality rate (red) versus regression predicted value (blue) 2002–2016. The squared correlation between the actual and predicted dfr is R

^{2}= 0.985.

**Figure 4.**ITS treatment effects for demonstration and follow-up periods. The left red bar shows the change in regression predicted value for the monthly drug-related fatality rate (dfr) during the baseline 2002–2006. The blue bar shows the significant ITS treatment effect (TE

_{1}) for the demonstration period. TE

_{1}< 0 supports Hypothesis 1, indicating a total reduction in the regression predicted value for dfr relative to the baseline trend. The first red bar on the right shows the significant TE

_{2}for the 2012–2014 follow-up subperiod. TE

_{2}> 0 supports Hypothesis 2, indicating a total 2012–2014 increase in the predicted value of dfr relative to the demonstration-period trend. Likewise, the significant TE

_{3}> 0 (right red bar) supports Hypothesis 2, indicating a 2015–2016 total increase in the predicted value of dfr relative to the demonstration-period trend.

**Figure 5.**Time series plot of observed dfr (red) and the counterfactual forecast of future values of dfr (blue) for the demonstration and follow-up periods (January 2007–December 2016). The ex-ante, dynamic, one-step-ahead forecasts [98] are based on the baseline data only.

Regression Coefficient | Coeffic. Estimate | Standard Error (SE) ^{1} | t-Ratio ^{2} |
---|---|---|---|

Constant term (β_{0}) | 4.369 | 0.669 | 6.54 ^{a} |

Lagged dep. variable (β_{1}) | 0.388 | 0.091 | 4.25 ^{a} |

Baseline trend (β_{2}) | 0.038 | 0.005 | 7.18 ^{a} |

Level shift Jan. 2007 (β_{3}) | −0.358 | 0.148 | −2.42 ^{d} |

Trend shift Jan. 2007 (β_{4}) | −0.027 | 0.005 | −5.27 ^{a} |

Trend shift 2012 (β_{6}) | 0.022 | 0.007 | 3.42 ^{b} |

Trend shift 2015 (β_{8}) | 0.114 | 0.016 | 6.96 ^{a} |

January seasonal (S_{1}) | 0.489 | 0.128 | 3.82 ^{b} |

February seasonal (S_{2}) | −0.285 | 0.145 | −1.97 ^{d} |

March seasonal (S_{3}) | 0.676 | 0.133 | 5.09 ^{a} |

April seasonal (S_{4}) | −0.297 | 0.150 | −1.98 ^{c} |

May seasonal (S_{5}) | 0.241 | 0.115 | 2.10 ^{c} |

June seasonal (S_{6}) | −0.371 | 0.137 | −2.71 ^{c} |

July seasonal (S_{7}) | 0.237 | 0.121 | 1.96 |

August seasonal (S_{8}) | −0.161 | 0.120 | −1.34 |

Sept. seasonal (S_{9}) | −0.579 | 0.097 | −5.91 ^{a} |

Oct. seasonal (S_{10}) | −0.157 | 0.098 | −1.59 |

Nov. seasonal (S_{11}) | −0.455 | 0.109 | −4.19 ^{a} |

Outlier Nov. 2015 | −1.250 | 0.088 | −14.30 ^{a} |

Outlier Dec. 2015 | −1.391 | 0.164 | −8.50 ^{a} |

Outlier Jan. 2016 | −0.577 | 0.206 | −2.81^{c} |

F-statistic: F(20, 159) = 536.30 (p < 0.001) | Mean (SD) of dfr = 11.166 (2.427) | ||

Root MSE = 0.311 | R^{2} = 0.985; Adjusted R^{2} = 0.984 | ||

Sum of squared residuals = 15.406 | Log-likelihood = −34.170 | ||

AICc ^{3} = 118.787 | BIC ^{4} = 182.586 |

Long-Run Equilibrium Coefficient (LR Multiplier) ^{1} | Coefficient Estimate | Standard Error (SE) ^{2} | t-Ratio ^{3} |
---|---|---|---|

Constant (b_{0}) | 7.137 | 0.132 | 54.20 ^{a} |

Baseline slope (b_{2}) | 0.062 | 0.004 | 16.30 ^{a} |

Level shift 2007 (b_{3}) | −0.585 | 0.183 | −3.20 ^{b} |

Trend shift 2007 (b_{4}) | −0.044 | 0.005 | −8.87 ^{a} |

Trend shift 2012 (b_{6}) | 0.037 | 0.007 | 4.96 ^{a} |

Trend shift 2015 (b_{8}) | 0.187 | 0.015 | 12.50 ^{a} |

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**MDPI and ACS Style**

Dillbeck, M.C.; Cavanaugh, K.L.
Empirical Evaluation of the Possible Contribution of Group Practice of the Transcendental Meditation and TM-Sidhi Program to Reduction in Drug-Related Mortality. *Medicina* **2023**, *59*, 195.
https://doi.org/10.3390/medicina59020195

**AMA Style**

Dillbeck MC, Cavanaugh KL.
Empirical Evaluation of the Possible Contribution of Group Practice of the Transcendental Meditation and TM-Sidhi Program to Reduction in Drug-Related Mortality. *Medicina*. 2023; 59(2):195.
https://doi.org/10.3390/medicina59020195

**Chicago/Turabian Style**

Dillbeck, Michael C., and Kenneth L. Cavanaugh.
2023. "Empirical Evaluation of the Possible Contribution of Group Practice of the Transcendental Meditation and TM-Sidhi Program to Reduction in Drug-Related Mortality" *Medicina* 59, no. 2: 195.
https://doi.org/10.3390/medicina59020195