Improvement in Heart Rate Variability Following Spinal Adjustment: A Case Study in Statistical Methodology for a Single Office Visit - Pages 17-22
John Hart

https://doi.org/10.6000/1929-6029.2019.08.03

Published: 11 May 2019

Abstract: Introduction: Statistical analysis is typically applied at the group level. The present study analyzes data during a single office visit as a novel approach providing real-time feedback to the clinician and patient regarding efficacy of an intervention. In this study, heart rate variability (HRV) was analyzed before versus after a chiropractic spinal adjustment.

Methods: The patient is an adult female who signed a consent form for the study. HRV was measured twice before a chiropractic adjustment and once afterwards using app-based technology. The three HRV values (two pre and one post) were then statistically analyzed using an online calculator for outliers using Grubbs test.

Results: The two pre-adjustment HRV (rMSSD) readings were consistently low: pre 1 = 16.0 milliseconds [ms] and pre 2 = 16.2 ms. The low HRV was an indicator that the patient’s nervous system was not functioning optimally. The patient’s atlas (C1) vertebra was palpated to be slightly out of alignment. These two findings (low HRV and vertebral misalignment) indicated the presence of a chiropractic subluxation (of the atlas vertebra). The subluxation was adjusted and within minutes the HRV increased (improved) to 27.5 ms. This improvement was calculated to be a statistically significant outlier (p < 0.05).

Conclusion: This study is an example of how statistical methods can be applied to the level of an individual patient during one office visit to assess neurological effectiveness of a chiropractic adjustment. Since this is a case study, the results may not apply to all patients. Therefore, further studies in other patients, and for longer follow-up times, are reasonable next steps.

Keywords: Chiropractic adjustment, heart rate variability, biostatistics, Grubbs test.

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Evaluation and Comparison of Patterns of Maternal Complications Using Generalized Linear Models of Count Data Time Series - Pages 32-39
Collins Odhiambo and Freda Kinoti

https://doi.org/10.6000/1929-6029.2019.08.05

Published: 08 July 2019

Abstract: Studying patterns of maternal complications is critical before, during and after childbirth. However, there is limited information on comparative trends of different maternal complications, particularly, in a resource-limited setting. In this study we fit six different types of maternal complications namely ante-partum haemorrhage (APH), eclampsia, obstructed labour, post-partum haemorrhage (PPH), ruptured uterus and sepsis to time series generalized linear model. We systematically compare the performance of the model in light of real data by checking its flexibility and serial correlation and the conditional distribution. We then, compute model fitting, assessment and prediction analysis for each maternal complication. Additionally, we provide a comparative review of the results by assessing the effect of intervention 1: basic emergency obstetric and new-born care (BEmONC) and intervention 2: comprehensive emergency obstetric and new-born care (CEmONC) services on trends in maternal complications. Results show that women with APH, eclampsia and obstructed labour at the time of delivery are significantly high. Maternal complication did not statistically vary by counties. The results of count GLM for APH showed presence of Intervention1 (BEmONC) reduces APH by a factor -0.189 (LCI =- 0.298, UCI= -0.0805) while CEmONC was not statistically significance. Similar inference is registered by PPH i.e. Intervention1 (BEmONC) is -0.17 (LCI =-0.258, UCI= - 0.082) while CEmONC remains insignificant. This can be interpreted to mean that public health facilities only require the basic minimum (BEmONC) infrastructure to cub APH and PPH. Mothers with sepsis and eclampsia were significantly more likely to experience maternal and perinatal deaths when delivering at facilities that lack BEmONC. Caregivers, who perform obstetric and maternal care, need be alert of maternal complications associated with PPH and obstructed labour. Introduction of BEmONC and CEmONC packages in maternal and neonatal clinics improved performance of caregivers in reducing maternal and pediatric complications and mortality.

Keywords: Maternal complications, Count Data time series, Trends, Goodness-of-fit, Conditional distribution.

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An Alternative Stratified Cox Model for Correlated Variables in Infant Mortality - Pages 23-31
K.A. Adeleke and A.A. Abiodun

https://doi.org/10.6000/1929-6029.2019.08.04

Published: 08 July 2019

Abstract: Often in epidemiological research, introducing a stratified Cox model can account for the existence of interactions of some inherent factors with some major/noticeable factors. This paper aims at modelling correlated variables in infant mortality with the existence of some inherent factors affecting the infant survival function. A Stratified Cox model is proposed with a view to taking care of multi-factor-level that has interactions with others. This, however, is used as a tool to model infant mortality data from Nigeria Demographic and Health Survey (NDHS) with g-level-factor (Tetanus, Polio and Breastfeeding) having correlations with main factors (Sex, infant Size and Mode of Delivery). Asymptotic properties of partial likelihood estimators of regression parameters are also studied via simulation. The proposed models are tested via data and it shows good fit and performs differently depending on the levels of the interaction of the strata variable Z*. An evidence that the baseline hazard functions and regression coefficients are not the same from stratum to stratum provides a gain in information as against the usage of the Cox model. Simulation result shows that the present method produces better estimates in terms of bias, lower standard errors, and or mean square errors.

Keywords: Stratified Cox, Semiparametric model, infant mortality, multifactor-level, confounding variables.

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Multivariate Analysis of Data on Migraine Treatment - Pages 40-50

Agostino Tarsitano and Ilaria L. Amerise

https://doi.org/10.6000/1929-6029.2019.08.06

Published: 26 August 2019

Abstract: Migraineur constitutes a multidimensional model of health disorder involving a complex combination of genetic, psychological, demographic, enviromental and economic factors. This model provides a framework to describe limitations of an individual functional ability and quality of life, and to aid in the elaboration of more adequate intervention programs for each patient. Our primary objective in this paper is a data-driven profiling of patients.

The approach followed consists of examining affinity/dissimilarity between sufferers on the basis of different family of indicators and then aggregating multiple partial matrices, where each matrix expresses a particular notion of the dissimilarity of one patient from another. One important particularity of our method is the notion of multi-dimensional dissimilarity for static and dynamic indicators, without ignoring any portion of data.

The partial dissimilarity matrices are assembled in the form of a global matrix, which is used as input of subsequent calculations, such as multidimensional scaling and cluster analysis. Our main contribution is to show how multi-scale, cross-section and longitudinal data from individuals involved in a migraine treatment program may optimally be combined to allow profiling migraine-affected patients.

Keywords: Kostecki-Dillon, General dissimilarity coefficient, Cluster analysis, Multi-dimensional scaling.

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