Modelling factors associated with therapeutic inertia in hypertensive patients: Analysis using repeated data from a hospital registry in West Africa.

The proportion of poorly controlled hypertensives still remains high in the general African population. This is largely due to therapeutic inertia (TI), defined as the failure to intensify or modify treatment in a patient with poorly controlled blood pressure (BP). The objective of this study was to identify the determinants of TI. We conducted a retrospective cohort study from March 2012 to February 2014 of hypertensive patients followed during 4 medical visits. The TI score was the number of visits with TI divided by the number of visits where a therapeutic change was indicated. A random-effects logistic model was used to identify the determinants of TI. A total of 200 subjects were included, with a mean age of 57.98 years and 67% men. The TI score was measured at 85.57% (confidence interval [CI] 95% = [82.41-88.92]). Measured individual heterogeneity was significantly significant (0.78). Three factors were associated with treatment inertia, namely the number of antihypertensive drugs (odd ratios [OR] = 1.27; CI = [1.02-1.58]), the time between consultations (OR = 0.94; CI = [0.91-0.97]), and treatment noncompliance (OR = 15.18; CI = [3.13-73.70]). The random-effects model performed better in predicting high-risk patients with TI than the classical logistic model (P value < .001). Our study showed a high TI score in patients followed in cardiology in Burkina Faso. Reduction of the TI score through targeted interventions is necessary to better control hypertension in our cohort patients.

A Whole Other Story: Interpreting Narrative Medicine.

The practice of conventional medicine is rooted in the ability of a patient to effectively communicate with their physician, and for the physician to comprehend the patient's story and perceive it through the lens of evidence-based practice. In reality, the differences in environmental backgrounds hinders this exchange of information and prevents a shared understanding and strategy. Narrative medicine provides a framework in which this divide can be bridged by encouraging the clinician to develop an appreciation of cultural nuances that drive a patient's decision making. The importance of this practice is highlighted with four stories in which the only path to competent patient management was through the utilization of narrative medicine.

Correcting inaccurate background mortality in excess hazard models through breakpoints.

BACKGROUND: Methods for estimating relative survival are widely used in population-based cancer survival studies. These methods are based on splitting the observed (the overall) mortality into excess mortality (due to cancer) and background mortality (due to other causes, as expected in the general population). The latter is derived from life tables usually stratified by age, sex, and calendar year but not by other covariates (such as the deprivation level or the socioeconomic status) which may lack though they would influence background mortality. The absence of these covariates leads to inaccurate background mortality, thus to biases in estimating the excess mortality. These biases may be avoided by adjusting the background mortality for these covariates whenever available. METHODS: In this work, we propose a regression model of excess mortality that corrects for potentially inaccurate background mortality by introducing age-dependent multiplicative parameters through breakpoints, which gives some flexibility. The performance of this model was first assessed with a single and two breakpoints in an intensive simulation study, then the method was applied to French population-based data on colorectal cancer. RESULTS: The proposed model proved to be interesting in the simulations and the applications to real data; it limited the bias in parameter estimates of the excess mortality in several scenarios and improved the results and the generalizability of Touraine's proportional hazards model. CONCLUSION: Finally, the proposed model is a good approach to correct reliably inaccurate background mortality by introducing multiplicative parameters that depend on age and on an additional variable through breakpoints.

Quantile regression for incomplete longitudinal data with selection by death.

Quantile regressions are increasingly used to provide population norms for quantitative variables. Indeed, they do not require any Gaussian assumption for the response and allow to characterize its entire distribution through different quantiles. Quantile regressions are especially useful to provide norms of cognitive scores in the elderly that may help general practitioners to identify subjects with unexpectedly low cognitive level in routine examinations. These norms may be estimated from cohorts of elderly using quantile regression for longitudinal data, but this requires to properly account for selection by death, dropout and intermittent missing data. In this work, we extend the weighted estimating equation approach to estimate conditional quantiles in the population currently alive from mortal cohorts with dropout and intermittent missing data. Suitable weight estimation procedures are provided for both monotone and intermittent missing data and under two missing-at-random assumptions, when the observation probability given that the subject is alive depends on the survival time (p-MAR assumption) or not (u-MAR assumption). Inference is performed through subject-level bootstrap. The method is validated in a simulation study and applied to the French cohort Paquid to estimate quantiles of a cognitive test in the elderly population currently alive. On one hand, the simulations show that the u-MAR analysis is quite robust when the true missingness mechanism is p-MAR. This is a useful result because computation of suitable weights for intermittent missing data under the p-MAR assumption is untractable. On the other hand, the simulations highlight, along with the real data analysis, the usefulness of suitable weights for intermittent missing data. This method is implemented in the R package weightQuant.

The Cameroon mobile phone SMS (CAMPS) trial: a protocol for a randomized controlled trial of mobile phone text messaging versus usual care for improving adherence to highly active anti-retroviral therapy.

BACKGROUND: This trial aims at testing the efficacy of weekly reminder and motivational text messages, compared to usual care in improving adherence to Highly Active Antiretroviral Treatment in patients attending a clinic in Yaoundé, Cameroon. METHODS AND DESIGN: This is a single-centered randomized controlled single-blinded trial. A central computer generated randomization list will be generated using random block sizes. Allocation will be determined by sequentially numbered sealed opaque envelopes. 198 participants will either receive the mobile phone text message or usual care. Our hypothesis is that weekly motivational text messages can improve adherence to Highly Active Antiretroviral Treatment and other clinical outcomes in the control group by acting as a reminder, a cue to action and opening communication channels. Data will be collected at baseline, three months and six months. A blinded program secretary will send out text messages and record delivery.Our primary outcomes are adherence measured by the visual analogue scale, self report, and pharmacy refill data. Our secondary outcomes are clinical: weight, body mass index, opportunistic infections, all cause mortality and retention; biological: Cluster Designation 4 count and viral load; and quality of life. Analysis will be by intention-to-treat. Covariates and subgroups will be taken into account. DISCUSSION: This trial investigates the potential of SMS motivational reminders to improve adherence to Highly Active Antiretroviral Treatment in Cameroon. The intervention targets non-adherence due to forgetfulness and other forms of non-adherence. TRIAL REGISTRATION: Pan-African Clinical Trials Registry PACTR201011000261458 http://clinicaltrials.gov/NCT01247181.