01/10/2023: 7:30 PM - 8:30 PM MST
Posters
Room: Kiva I,Silverman Foyer
Presentations
A national additional risk minimization measures (aRMMs) program was implemented to train pharmacists for safe supply of non-prescription Viagra Connect® (VC) to erectile dysfunction (ED) patients in United Kingdom (UK). This survey evaluated the effectiveness of aRMMs. This cross-sectional, web-based survey enrolled ED patients who purchased ≥1 supply of VC in UK, using a structured self-administered questionnaire. Patient responses were analyzed using descriptive statistics. The final sample had 297 patients, who reported that pharmacists assessed their suitability for VC by inquiring about their blood pressure and heart comorbidities (91.9%), other relevant illnesses (87.9%), medications (86.5%) and ED diagnosis (82.2%) and advised them to consult their doctor regarding ED (51.2%). Additionally, 65.0% of patients had either consulted (19.2%) or planned to consult (45.8%) their doctors; 85.5% were advised on how to take VC correctly, 82.2% on possible side effects for which they might have to discontinue taking VC and consult their doctor, 80.1% were informed that ED can be caused by underlying conditions such as high BP, diabetes, high cholesterol, and heart disease, and 68.7% received advice on lifestyle modifications from their pharmacists. In general, if the participants reported having ED, the pharmacists were more likely to assess their suitability for VC and provide advice on appropriate use of VC and on their need to see doctors. Additional results were presented at ICPE 2022. Overall, the results from this survey provide reasonable confirmation of the effectiveness of the VC aRMMs program and assurance that ED patients, when requesting and purchasing VC in UK pharmacies, were assessed appropriately for suitability of VC and receive the appropriate advice from pharmacists.
Disclaimer:
This study was sponsored by Upjohn, a legacy Pfizer division, now merged with Mylan to be Viatris. The views and opinions expressed in this presentation are the author's own and should not be attributed to Viatris, its directors, officers, employees, or affiliates, or any organization with which the presenter is employed or affiliated.
Presenting Author
Kelly Zou, Viatris Inc.
First Author
Jim Li, Viatris
CoAuthor(s)
Joanna Lem, Pfizer Inc
Muhammad Younus, Pfizer Inc
David Taylor, University College London
Shaantanu Donde, Viatris Inc.
Janine Collins, UBC
Kelly Zou, Viatris Inc.
We propose a cross-temporal design that uses a time-related variable as an instrumental variable for evaluating studies where the treatment is not directly controlled, and important confounders cannot be adjusted due to data limitations. Over time, the rising accessibility of a particular treatment has prompted certain people to take the treatment. In other words, if it were before, they would not have used the treatment. By taking advantage of this rise over time, we formulate an encouragement design using subjects observed from two-time points and partition them into three strata: always-takers, never-takers, and compliers, based on their potential receipt of the treatment at different time points. Because the time may impact the potential outcomes regardless of the receipt of treatment, the usual exclusion restrictions assumptions are violated. Therefore, we introduce the common trends assumption and the weakly identifying assumption to identify the temporal effect and separate it from the desired treatment effect among compliers. The weakly identifying assumption allows the temporal effects of each stratum to be different and correlated. It introduces a hierarchical structure to the data so that the temporal effect of compliers can be estimated through borrowing information from the other two strata. In estimation, we present two approaches for identifying stratification: the cross-temporal matching and the data augmentation (DA) algorithm, and model the data using Bayesian analysis. The simulation results show that the DA with the common trends assumption provides robust estimation performance even when the assumption is violated. Further, given the growth in the Medicare Advantage program (MA) enrollment, we applied the proposed method on estimating the effect of MA on the risk of nursing home residents who were admitted from acute hospital being re-hospitalized in the 30 days after their hospital discharge as compared to the traditional program.
Presenting Author
Yi Cao
First Author
Yi Cao
CoAuthor(s)
Roee Gutman, Brown University
Pedro Gozalo, Brown University
Abstract: Buprenorphine is a highly effective but underutilized treatment for opioid use disorder (OUD). In April 2019, New Jersey Medicaid implemented a set of policies to improve buprenorphine access and use that eliminated prior authorizations, increased reimbursement for office-based addiction treatment, and established regional centers of excellence to support treatment providers. We evaluated policy-related changes in buprenorphine use and treatment duration among NJ Medicaid enrollees with OUD, as well as changes in access to treatment providers related to shifts in buprenorphine prescribing. Using autoregressive integrated moving average models in an interrupted time series framework, we assessed the association of the Medicaid policies with buprenorphine receipt, retention, and prescribing. We created monthly time series from 2016-2020 to examine trends in the 1) rate of buprenorphine receipt per 1,000 beneficiaries with OUD; 2) percentage of new buprenorphine episodes lasting 30+ days; and 3) rate of buprenorphine prescribing per 1,000 total Medicaid prescribers, overall and by specialty. The sample included 21,439 Medicaid beneficiaries with diagnosed OUD and 2,569 buprenorphine prescribers. Policy implementation was associated with a monthly increase in the number of patients who received buprenorphine of 1.45 per 1,000 enrollees (95% CI, 1.05 to 1.86), more than doubling the pre-policy trend. However, the monthly trend in the percentage of enrollees with new buprenorphine episodes who were retained for 30 or more days decreased following implementation (-0.17%; 95% CI, -0.25, -0.08). Policy implementation was also associated with increases in the number of buprenorphine prescribers at the time of implementation (1.57 per 1,000 total prescribers, 95% CI, 0.23 to 2.92) and over time in the post-policy period (0.38; 95% CI, 0.29 to 0.48). Estimates were similar across provider specialties, but increases were most pronounced among primary care and emergency medicine physicians. Overall, the findings indicate that NJ Medicaid policies expanded buprenorphine access for OUD treatment, particularly in general medical and acute care settings. However, the decrease in treatment episodes lasting 30 or more days indicates that retention on buprenorphine remains a challenge and highlights the need for further research on effective strategies to improve treatment continuity in conjunction with continued initiatives to increase treatment uptake.
Relevance to contemporary health policy, conference theme, and diversity mission: This study uses complete and current state-level administrative health care data (NJ Medicaid claims), comprising a priority group for efforts to address the ongoing overdose epidemic. With the goal of generating evidence on policy effectiveness, the findings are shared with state Medicaid partners to inform evolving programs, demonstrating the value of data analysis for influencing uptake of evidence-based policies. The first and second authors are PhD students in the Schools of Social Work (Treitler) and Public Health (Nowels) at Rutgers University and would benefit from attending this conference to further develop methodological skills needed to generate rigorous evidence to inform policy and practice.
Presenting Author
Peter Treitler, Rutgers, The State University of New Jersey
First Author
Peter Treitler, Rutgers, The State University of New Jersey
CoAuthor(s)
Molly Nowels
Hillary Samples
Stephen Crystal, Rutgers, The State University of New Jersey
Neighborhood-level social determinants of health, such as access to pharmacy, may exacerbate health disparities. However, differential pharmacy access in an urban environment, such as New York City (NYC), and its impact on medication adherence among residents, is not clearly understood. In this work, we aim to characterize variations in pharmacy access in NYC neighborhoods and assess social and neighborhood characteristics of low pharmacy access.
Data on number of pharmacies per 10,000 residents (pharmacy densities) within a census tract were collected from the National Neighborhood Data Archive. Local indicators of spatial association (LISA), such as Local Moran Index and Geti Ord Statistic, were used to identify clusters of census tracts with systematically higher (hot-spot) or lower (cold-spot) pharmacy densities than expected under random spatial distribution. The hot-spot and cold-spot census tracts were then summarized using census tract level socio-economic status (SES) index (between 0-100), vehicle access and other demographic variables, derived from 5-year estimates of the U.S. Census Bureau's 2019 American Community Survey as well as walkability from the City Health Dashboard.
Of the 1,941 census tracts in NYC, 21 were identified to have lower than expected pharmacy densities, whereas 18 had higher than expected pharmacy densities. Neighborhoods with low pharmacies were all either in Brooklyn (9/21, 42.9%) or Queens (12/21, 57.2%), and had a median value of 0 (IQR= 0, 0.98), compared to the overall NYC median of 2.5 (IQR=0, 5.5) pharmacies per 10,000 residents. Neighborhoods with the lowest pharmacy density had the highest proportion of non-Hispanic Black residents (44.6%; SD=34.9%) and lowest SES (mean = 44.6, SD = 11.9), compared to 4.8% (SD = 9.3%) non-Hispanic Black residents and mean SES of (67.2, SD = 19.9) in the high pharmacy density neighborhoods. The association between differential access to pharmacies and medication non-adherence among patients with heart failure will also be presented. As a next analytical step, we will implement Bayesian spatial conditional autoregressive models to model pharmacy densities adjusting for multiple neighborhood characteristics.
Our findings highlight inequities in pharmacy access and support investigating place-based approaches to inform policies that address neighborhood-level health disparities among NYC residents.
Presenting Author
Steven Lawrence, Grossman School of Medicine at New York University
First Author
Steven Lawrence, Grossman School of Medicine at New York University
CoAuthor(s)
Amrita Mukhopadhyay, NYU
Saul Blecker, NYU
Samrachana Adhikari, NYU School of Medicine
Differences between the sample of individuals participating in biomedical research and larger populations relevant to health policy questions constitute an important bottleneck in the pipeline from therapeutic discovery to clinical application. Recent work-rooted in the tools of causal inference-has made important advances in tackling this problem in the context of meta-analysis. That is, given a target population of interest and a set of results from possibly several clinical trials, how can we transport the results from those trials to the target population in a way that adjusts for heterogeneity in baseline covariates? Roughly, the current state-of-the art in addressing this question involves pooling individual patient data from the set of clinical trials and subsequently applying one of several techniques from causal inference in order estimate the effect of interest as observed in the pooled trial population had the treatment been applied in the target population.
While such covariate-induced heterogeneity constitutes an important way in which the target population may differ from the set of trial participants in a meta-analysis, further sources of heterogeneity remain. In particular, many of the same concerns implicated in the development of random-effects meta-analysis apply equally in this case. Numerous differences in trial conduct between the set of trials included in the meta-analysis may persist even after accounting for covariate differences. For instance, if clinicians at two trial sites applied distinct versions of a given treatment, we might expect systematic differences in trial outcomes even if the same individual participated in both trials. Under a circumstance where one such site has a much larger sample size than the other, pooling their data and adjusting for covariate differences with the target population may inadvertently overrepresent the treatment version applied in the larger trial. Across the numerous, varied settings of real-world clinical practice, we might more reasonably assume that each such version would be applied with equal probability.
Our work builds on existing developments in causally-interpretable meta-analysis by accounting for this residual between-trial heterogeneity, which persists even after adjusting for differences in baseline covariates. We develop causal estimands and corresponding estimators that explicitly reference residual heterogeneity and, moreover, help to clarify the conditions under which we might still make meaningful, policy-relevant conclusions within a target population. In general, transporting results from a set of clinical trials to a target population is highly relevant to health policy, as policies which incorporate information on the costs and benefits of treatments are often designed to apply to diverse populations that may differ significantly from participants in clinical research. Equally diverse are the clinical settings in which such treatments are ultimately put into practice. Our work aims to extend recent methodological advances addressing the former type of diversity by also accounting for the latter. By doing so, we hope to expand the scope of clinical research to make conclusions that are more actionable, policy-relevant, and precise.
Presenting Author
Justin Clark
First Author
Justin Clark
Objective
Clinical prediction models providing binary categorisations for clinical decision support require the selection of a probability threshold, or 'cutpoint', to classify individuals. Existing cutpoint selection approaches typically optimise test-specific metrics, including sensitivity and specificity, but overlook the consequences of correct or incorrect classification. We introduce a new cutpoint selection approach considering downstream consequences using net monetary benefit (NMB) and through simulations compared to alternative approaches in preventing inpatient falls.
Materials and Methods
Parameter estimates for costs and effectiveness from prior studies were included in Monte Carlo simulations. For each use case we simulated the expected NMB resulting from the model-guided decision support using a range of cutpoint selection approaches, including our new value-optimising approach. Sensitivity analyses applied alternative event rates and model discrimination performance.
Results
The proposed approach that considered expected downstream consequences was frequently NMB-maximising compared to other methods. Sensitivity analysis demonstrated that it was or closely tracked the optimal strategy under a range of scenarios. Under scenarios of relatively low event rates and discrimination that may be considered realistic for falls (prevalence=0.036, AUC=0.70), our proposed cutpoint method was either the best or similar to the best of the compared methods regarding NMB.
Discussion
Our results highlight the value of conditioning cutpoints on the implementation setting, particularly for rare and costly events, which are often those subject to prediction model development research.
Conclusions
This study proposes a cutpoint selection method that may optimise clinical decision support systems towards value-based care.
Presenting Author
Rex Parsons
First Author
Rex Parsons
CoAuthor(s)
Robin Blythe, Australian Centre for Health Services Innovation and Centre for Healthcare Transformation, School
Susanna Cramb, Queensland University of Technology
Steven McPhail, Queensland University of Technology
Introduction
In Japan, during the 1960s, a limited number of prefectures started regional cancer registries. However, in 2016, the operation of nationwide cancer registration began, based on the Act on Promotion of Cancer Registries. Since this time, the government has collected data on all cancer patients from all hospitals in Japan. For the first time, we can report cancer survival based on nationwide cancer registry data. Health inequalities have become of increasing concern in Japan and the government has been targeting a "reduction in the gap in healthy life expectancy" in the national health plan "Health Japan 21" since 2013. The National Cancer Plan includes "equalization of cancer treatment" as a goal but a concrete target was not set as there had been no monitoring of inequalities in cancer survival based on the population-based cancer registry data.
Methods
We analyzed 893,560 cases, first primary malignant cancer patients diagnosed in 2016 and followed-up for at least one year. We calculated one-year net survival by sex, cancer site, stage at diagnosis and quintile of areal deprivation index using the Pohar-Perme method. Background mortality was estimated from the national lifetables of Japan. We used municipality-level areal deprivation indexes to categorize the quintile of the area-based socioeconomic group.
Results
Absolute difference between one-year net survival of patients in the least deprived area and those in the most deprived area ranged from 0.2% (smallest gap, gallbladder) to 12.6% (widest gap, leukemia) in men and -1.3% (kidney) to 8.0% (brain) across cancer sites. By cancer site for all stages, the widest gap was observed for leukemia in men and brain and central nervous system cancer in women. For localized patients, the widest gap in one-year net survival was observed in pancreatic cancer in men and women. For distant metastasis patients, the widest gap was observed in thyroid cancer in men and oral cancer in women.
Discussion
We reported the socioeconomic inequalities in one-year net survival using nationwide population-based cancer registry data, for the first time, in Japan. Even in short-term survival, we observed a wide gap in one-year net survival between patients in the least deprived and the most deprived areas, depending on the cancer site and stage at diagnosis. Further analysis will be needed, using a multivariate excess hazard model, to control the differences in age distribution among the quintiles of the areal deprivation groups. Differences in stage at diagnosis among the socioeconomic groups might also influence the gaps in cancer survival. In order to use these findings as target figures to monitor inequalities in cancer outcome, as an assurance of equity among cancer treatment, other important factors, such as access to specialized hospitals, patient comorbidities, and performance status, also need to be considered. We also need to understand the mechanisms of socioeconomic inequalities in cancer outcome, in order to deal with the issue.
Presenting Author
Yuri Ito, Osaka Medical College
First Author
Yuri Ito, Osaka Medical College
CoAuthor(s)
Keisuke Fukui, Osaka Medical College
Kota Katanoda, National Cancer Center, Japan
Tomoki Nakaya, Tohoku University
Takahiro Higashi, National Cancer Center, Japan
Tomotaka Sobue, Osaka University
Purpose: Depression is one of the most common comorbid psychiatric disorders for breast cancer patients and can decrease patient's quality of life and negatively affect cancer treatment results if untreated. Our goal was to identify treatment barriers to women with breast cancer who sought psychotherapy for depression. Such findings may help policy makers and researchers with decision making when funding and designing future studies that involve this population, especially in communities with high rates of health disparities.
Methods: We used data from a randomized trial for women with breast cancer and current diagnosis of non-psychotic unipolar major depressive disorder (MDD). Patients were randomly assigned to 12 weeks of one of three psychotherapies and attrition was assessed by whether or not subjects completed 12 weeks of treatment sessions. Descriptive analyses and logistic regression were used to identify barriers. R shiny was used to determine the residence of study participants.
Results: 134 patients were randomized of whom 104 (77.6%) were Hispanic. 59 (44%) were either nonstarters or dropouts and 49 (83.1%) of them were Hispanic. Being a Hispanic woman, less educated and having more distant residence significantly predicted attrition. Single Hispanic mothers had significantly higher attrition risk than married and/or childless women.
Conclusion: Identifying barriers to treatment is important to improve treatment adherence for depressive women with concurrent diagnosis of breast cancer, especially for traditionally underserved minorities with geographical distance from hospitals and being as a single parent. Additional support, such as affordable tele-medicine, language assistance, financial aid for transportation/child-care, and allocating more funds should be considered to improve treatment adherence and outcomes.
Presenting Author
Ying Chen
First Author
Ying Chen
CoAuthor(s)
Daniela Quigee, Columbia University
John Markowitz, New York State Psychiatric Institute
Carlos Blanco, National Institute on Drug Abuse
Joy Zhang, New York State Psychiatric Institute & River Dell Regional High School
David Hellerstein, New York State Psychiatric Institute
2023 Survey of Non-Communicable Diseases and their Risk Factors
Although the incidence of Non-Communicable Diseases (NCDs) is rising globally, the growing burden of NCDs in low- and middle-income countries presents major challenges for health systems. Despite the communicable diseases and other conditions still being predominate in sub-Saharan Africa, NCDs are projected to become the leading cause of death by 2030. As in the rest of the developing countries, Kenya is experiencing an epidemic transition in its diseases burden from communicable diseases to non-communicable conditions resulting in a double burden of disease.
Non-Communicable Diseases are a major public health concern with significant socio-economic implications in terms of health care-needs, lost productivity and premature death. These diseases remain to be a serious setback to the attainment of social, health and economic targets if no proper interventions are put in place. The NCDs accounts for about a half of total hospital admissions and over 39 per cent of hospital deaths in Kenya. The major NCDs are cardiovascular conditions, cancers, diabetes, and chronic obstructive pulmonary diseases. Equally contributing to the huge burden are violence and injuries, mental disorders, oral, eye and dental diseases.
To address the health concerns attributed to the NCDs, a baseline STEPS survey was done in 2015 to collect comprehensive information on risk factors for NCDs. STEPS is a WHO standardized NCD surveillance protocol involving three different levels of steps to gather self-reported data on demographics and behavioural risk factors, physical and biochemical measures from nationally representative populations. 2015 STEPS survey was a national cross-sectional household survey designed to provide estimates for indicators on risk factors for NCDs for persons' age 18 – 69 years. The results obtained from the survey helped to establish interventions that are based on local risk factor burden and forms a resource to inform the process of planning and policy formulation as well as a monitoring and evaluation tool for NCDs.
In light of the rising burden of NCDs and of government efforts to control NCDs in Kenya, NCD risk factor surveillance should be a priority for the national health information system. Considering that the STEPS survey was conducted almost 8 years ago, the country needs to conduct periodic surveys geared towards monitoring and evaluation of NCD intervention mechanisms. The baseline results also informed the need for collecting subnational (county) indicators to guide NCD surveillance and monitoring at that level.
Consequently, a survey of NCDs and their risk factors is planned to be implemented in the year 2023/24 financial year across the country. This survey will be implemented by the Kenya National Bureau of Statistics (KNBS) Health Statistics Unit in collaboration with the Ministry of Health (MoH) Division of Non-Communicable Diseases. The survey is aimed at collecting accurate data that will inform the national and county level policy makers to make appropriate decisions of tackling the burden of the disease. Preparation of the survey are in progress and it is envisaged that the survey will be rolled out as per the plan.
Key Words: Risk factors, Non-Communicable Diseases, Steps, Survey, Monitoring, Evaluation
Presenting Author
Elias Rutere
First Author
Elias Rutere
Statistical inferential results generally come with a measure of reliability for decision-making purposes. Consumers who implement policies based upon statistical results may find it difficult or impossible to determine whether the producers of those results are overstating reliability. This information asymmetry between producers and consumers can lead to an adverse selection problem where, at best, the full benefits of a policy are not realized or, at worst, a policy is deemed too risky to implement at any scale. Producers can remedy this by offering a performance guarantee to consumers. Producers can overcome their own risk aversion and wealth constraints by sharing risks with other producers. The problem and remedy are illustrated using a confidence interval for the success probability of a binary policy outcome.
Presenting Author
Duncan Ermini Leaf, University of Southern California
First Author
Duncan Ermini Leaf, University of Southern California
The physical and mental development of pupils could be hampered by inadequate food intake and infections, with immediate and long-term consequences. This has implications for poor school performance, lower intelligent quotient, poor psychosocial development, and reduced cognitive functioning. This study evaluated the growth and distribution of body fat between pupils from 5 to 19 years old. A cross-sectional study was conducted among pupils from five primary schools in different communities around the Kainji Dam. This assessment was based on a comparison with the reference standards set by the World Health Organization to determine their deviations. A qualified nurse took physical measurements for age, height, and body weight using an electronic scale and stadiometer. The data was analyzed with Anthroplus v1.0.4 software. The overall prevalence of stunting was 21.5%, with 8% having severe stunting. The prevalence of thinness was 35.2% with wasting of 11.2%, while the overall prevalence of underweight for children > 10 years was not calculated. The prevalence of underweight in children aged 5 to 10 years was 16.9%, with 2.4% being severely underweight. The curves for boys and girls deviate from the normal distribution. Women have a higher percentage of stunted growth than men. This study showed a high prevalence of thinness and stunting, which is a consequence of poor diet resulting from multiple and interrelated circumstances such as poor diet, dietary practices, and repeated infections. Interventions such as Home Grown School Feeding Scheme of the government are a right step to improve the nutritional status of school pupils in rural communities in Nigeria and this should be accepted as a policy.
Presenting Author
Dayo Lawal, Nigerian Institute of Medical Research
First Author
Babatunde Adewale, Nigerian Institute of Medical Research
CoAuthor
Kazeem Osuolale, Nigerian Institute of Medical Research (NIMR)
Homeostasis is the maintenance of physiologic constancy and the various mechanisms by which living organisms regulate their internal environment. Homeostasis causes operating variables such as glucose level, body temperature, or blood pressure to be maintained close to optimal set points, e.g., Sweet Spots. Homeostatic imbalance occurs when normal physiologic control is disrupted, with aging being a major factor in such dysregulation. The Canadian Institute for Health Information projects an increase in the burden on the healthcare system due to the prevalence of the over-65 age group among Canada's population. This demographical situation raises the importance of discovering of new indicators of homeostenosis, lifestyle factors associated with physiological dysregulation, and further development of preventive policies.
Identifying homeostatically-controlled traits across multiple domains and data sources will extend our knowledge about biological pathways and networks involved in homeostasis of that measures. Examining the genetic effects on distance from each sweet spot will inform insights into genetic architecture of homeostasis in health and may allow re-purposing of known drugs or development of new drugs to preserve the homeostatic condition across the lifespan. A nuanced health report-card for individuals based on deviation from optimal values could predict future health outcomes and motivate to take actions before they are outside of the clinically normal ranges. Further, understanding the lifestyle and socioeconomic factors with effect on phenotypic sweet spot distance would allow development of individualized health-preservation plans.
Studying successful aging allows us to identify previously unrecognized features of healthy aging. The Super Seniors Study recruited healthy oldest-old and population-based mid-life controls to study genetic factors associated with healthy aging. Super Seniors are healthy individuals aged 85–110 who at recruitment had never been diagnosed with cancer, cardiovascular disease, major pulmonary disease, dementia, or diabetes. We previously reported that the group of Super Seniors show reduced telomere length variance compared to the middle-aged group. They also show lower variance in some hematology parameters compared to age-matched less healthy seniors. We hypothesized that the healthier group has physiological measures closer to a Sweet Spot, and therefore lower variance, for traits that are relevant for healthy aging. This study aims to discover new physiologic measures relevant to healthy aging and homeostasis, and lifestyle and genetic factors associated with optimal homeostatic regulation.
We examine data from the Canadian Longitudinal Study on Aging (CLSA), including 30,097 participants aged 45 to 85 years. Physiological variables across eight domains and untargeted plasma metabolomic profiling were used for investigation. Using five instruments, we calculated health scores for CLSA Comprehensive cohort participants. Homogeneity of variance was pairwise tested between the most healthy and least healthy instrument levels. We also characterized the relationship between each instrument and phenotype magnitude using segmented regression to determine sex and age-specific optimal values.
Initial findings validate the proposed approach and underscore the relevance of applying a homeostasis lens to human physiology. In total, 142 physiological measures and 94 metabolites revealed heteroskedasticity. Moreover, a positive association between total dysregulation and health decline confirmed that having a value further from Sweet Spot, in either direction, correlates with greater levels of health deficits. Future work to examine the genetic and lifestyle effects on distance from each sweet spot will inform insights into how to optimize and preserve health.
Presenting Author
Olga Vishnyakova, Simon Fraser University
First Author
Olga Vishnyakova, Simon Fraser University
CoAuthor(s)
Xiaowei Song, Fraser Health
Kenneth Rockwood, Division of Geriatric Medicine, Dalhousie University, Halifax, NS, Canada
Lloyd T. Elliott, Actuarial Science, Simon Fraser University
Angela Brooks-Wilson, Canada’s Michael Smith Genome Sciences Centre
Weighting is a general and often-used method for statistical adjustment. In observational studies and sample survey settings, one objective of weighting is to balance covariate distributions. An additional objective is that the weights be "small" in the sense that they have minimal dispersion and therefore produce a more stable estimator. There are two broad approaches to weighting: a modeling approach that targets these objectives by maximizing the fit of a propensity score model, and a balancing approach that directly optimizes the weights toward these two objectives. While the balancing approach tends to exhibit better performance in practice, at present it is not feasible to implement it in the increasingly common setting of very large observational studies when investigators wish to balance broad classes of functions of the covariates. Here, we propose a novel algorithm for scalable kernel-based stable balancing. We focus on a particular form of the balancing approach to weighting which posits a quadratic programming problem to solve for the weights of minimum variance that approximately balance the covariates. In order to choose what to balance, we use the kernel balancing approach that allows us to assume that the outcome regression functions lie in a large, flexible function space associated with a kernel, thus offers an effective way to minimize the bias caused by covariate imbalance. Based on the Nÿstrom method, the corresponding kernel-based imbalance metrics are constructed in linear time and space and incorporated into our quadratic program as linear constraints. Then we show that our balancing estimator can be efficiently computed by solving the quadratic program using the specialized first-order alternating direction method of multipliers. In extensive simulation studies reflecting a variety of data settings, we show that our proposed approach can handle large datasets containing millions of observations in seconds without sacrificing estimator accuracy. We apply our methods in a national study of heart attack treatment and outcomes by hospital profit status with 1.27 million patients. After weighting, we observe that for-profit hospitals perform percutaneous coronary intervention at similar rates as other hospitals; however, their patients have slightly worse mortality and higher readmission rates.
Speaker
Bijan Niknam, Harvard University
First Author
Kwangho Kim, Harvard Medical School
CoAuthor(s)
Bijan Niknam, Harvard University
Jose Zubizarreta
Several studies find a positive relationship between antibiotic use in the first year of life and obesity in early childhood, or a higher risk of obesity with use of broad-spectrum antibiotics or multiple antibiotic courses. Most recommend the judicious use of antibiotics; however, this medical literature often fails to account for confounding indication from infections. The objectives of this research are to 1) determine if confounding by indication provides an explanation in the basic antibiotics-obesity analysis, 2) to test whether antibiotic use is a mediator of the infection – obesity relationship, and 3) examine which types of infection might drive the infection – obesity relationship. We demonstrate how a subgroup analysis of children from low income families utilizing Medicaid claims records can be used to test these hypotheses. A validation of the analysis is performed on a second dataset of merged EMR and Medicaid data to examine BMI measurements.
Presenting Author
Adrienne Ohler, University of Missouri
First Author
Adrienne Ohler, University of Missouri
CoAuthor
Amy Braddock, University of Missouri
Background: While there is a wealth of evidence on the direct relationship between dietary sodium intake and blood pressure, the evidence of its relationship with risk of cardiovascular diseases (CVDs), and major events, has been inconsistent, due to limitations including confounding, measurement errors, and rarely examining the role of other risk factors, which should be considered to inform solid scientific evidence-based prevention guidelines.
Aim: To assess the association of sodium intake with cardiovascular disease (CVD) and all-cause mortality, among US adults aged 20 years and older.
Method: We used a nationally representative sample of 25,075 US adults, followed from time of survey participation through December 31, 2015. We examined data from the 2015 public-use National Health and Nutrition Examination Survey Linked Mortality File (1999-2014). Incident all-cause, and CVD mortality status were determined by assigned vital status codes and further determination from ICD, 10th Revision (ICD-10), and linked sodium, estimated by single 24-hour dietary recall. Bivariate analyses were performed; and relative risks (RR) for CVD (HD) and all-cause mortality were calculated from multivariate adjusted Poisson regression models, and further stratification models, accounting for the sampling design.
Results: During a mean follow-up period of 135 (33.3) months, 3,371 deaths were documented, including 755 CVD, and 561 HD deaths. After multivariate adjustment, comparing the lowest quartile of sodium consumption, higher sodium intake quartiles were significantly associated with decreased all-cause mortality, quartile 3 (RR=0.712; 95%CI:0.582, 0.871), and quartile 4 (RR=0.767; 95%CI:0.597, 0.984). In stratified models, the findings differed significantly by sex, and age. The risk for all-cause mortality remained consistently decreased across all quartiles: quartile 2 (RR=0.851, 95%CI: 0.731, 0.991), quartile 3 (RR=0.747, 95%CI: 0.608, 0.918), and quartile 4 (RR=0.703; 95%CI: 0.510, 0.970) among older participants, and females (quartile 3 (RR=0.607; 95%CI: 0.456, 0.806); whereas higher sodium intake was associated with increased heart disease (HD) mortality risk among participants who were younger than 65 years (quartile 3 (RR=2.639; 95%CI: 1.094, 6.367), and quartile 4 (RR=3.436; 95%CI: 1.020, 11.578) and among females (quartile 2 (RR=1.927; 95%CI:1.075, 3.457). Risk was also decreased for heart disease mortality in older participants in the third quartile (RR=0.423; 95%CI:0.223, 0.804).
Conclusion: The direct evidence of the harmful effects of high sodium intake among US adult population aged 65years and younger, suggest that a reduction in sodium intake may play a key role in the prevention of non-fatal and fatal CVDs in this population. However, the inverse relation seen for all-cause, and heart disease mortality among older participants raises questions regarding the likelihood of a survival advantage accompanying a lower sodium diet. Further study is warranted to better define the mechanism and role of sodium intake as a potential modifiable risk factor for mortality outcomes.
Presenting Author
Maryglad Komo, SUNY Downstate School of Public Health
First Author
Maryglad Komo, SUNY Downstate School of Public Health
A prospective analysis to provide Community Health Center patients needing weekly medical care with housing accommodations within a fifteen (15) minute travel radius of treatment facilities within the scope of one mid-sized city. Housing capacity planning would be based upon aggregate medical needs within the geographic area, similar to manufacturer capacity planning for custom medical devices with extended delivery timeline requirements. Medical provider could prospectively request a housing unit upon appropriate diagnosis, similar to other anticipated medical equipment.
Presenting Author
Katherine Wellington, University of Massachusetts Amherst
First Author
Katherine Wellington, University of Massachusetts Amherst
Background and Methods: Suicide is a leading cause of death in the US. Substance abuse is a known risk factor for suicide. The exact correlation between substance abuse and suicide is unknown. In addition, the potential years of life lost due to suicide is unknown. A 10-year review (2007-2016) of self-inflicted injury individuals in the National Trauma Data Bank (NTDB) is performed. Pearson Chi-square statistical test and multiple logistic regressions are utilized for the analysis.
Results: Our results also indicate that those tested positive for substance abuse have a higher Hospital Discharge Disposition death rate compared to those who were tested negative, 56.79% who died were tested positive. We also found that Years of Potential Life Lost (YPLL) from suicide is 224603 for Whites, 31156 for Blacks, 3054 for Native Americans, 5474 for Asians, 38758 for Hispanics, $106806$ for 'Unknown', and $6594$ for others. The most common methods of suicide are Cut/Pierce (40.56%), Firearm (31.26%), and Fall (10.50%), and the remaining percentage for other means of suicide; this is strongly associated with substance abuse (p-value< .001). Those who committed suicide by Cut/Pierce 61.81% of them were drug tested positive, by Firearm 61.24%, and by Fall 60.44%. Our results also indicate that death rate related to illicit drug is slightly higher than prescription drug (see figure).
Among the self-inflicted injury in the sample of size 44683, 52.64% percentage of them are tested positive of illicit substance usage and the remaining are tested negative. And also, those with severe traumatic brain injury, 51.98% are tested positive of illicit substance use.
By race group, the distribution of illegal use of drug is as follow: 59.06% of self-inflicted injuries Blacks are tested positive for illicit drug, 50.22\% for self-inflicted injuries Whites and 55.24% for self-inflicted injuries Hispanics. .
Conclusions This paper provides a succinct overview of substance abuse (illicit and prescribed) and by race groups using trauma database. The paper also highlights some potential years of life loss due to self-inflicted injuries. In addition, the paper points out the distribution of methods of suicide.
Keywords: Illicit and Prescribed Drug, Potential Years of Life Loss
Presenting Author
Demba Fofana
First Author
Demba Fofana
CoAuthor(s)
Joy Alvarado, UTRGV
Sidketa Fofana, UTRGV
Jeffrey Skubic, DHR
Background
Electronic health record (EHR) data have been equated to the holy grail of health services research-offering comprehensive access to clinical data to answer research questions. This type of dataset is especially important in an intensive care setting, where ventilated subjects often utilize a number of life support and monitoring technologies which stream longitudinal data into the EHR.
Fortunately, much of these data are discretely structured as numerical data, along with units of measurement and a timestamp for when the measurement was taken, or a ventilator setting was recorded. However, these data are often collected at irregular intervals reflective of the dynamic nature of critically-ill patients whose monitoring and support needs often fluctuate. This irregularity presents a challenge when analysts use these data to reconstruct a clinical picture that can be leveraged to create an analyzable dataset; none of the times align where some measurements may be misaligned by seconds and others by hours.
Methodological Aim
Our team had to solve the problem of aligning a multitude of irregular, sparse data from subjects in an intensive care setting who were ventilated. To our knowledge, this problem has not been addressed for this clinical population-demonstrating the innovation of this methodological approach. While this method was employed in SAS (using PROC SQL), it could easily be used in other statistical software such as R or Stata.
Methods
We created a long file whereby for each subject a date/time entry was created for each minute of intubation-from the first to the last date/time of intubation, along with the subject identifier. For example, if a subject was intubated for 5 full days there would be 7,200 rows for that subject, containing two columns: subject identifier and a date/time. For each of the clinical measures we wished to add to the table, the original date/time of that measurement would be rounded to the nearest minute. Then the tables would be left-joined using PROC SQL by subject identifier and date/time.
After ample discussion with our clinical collaborators, we felt confident that the time between recorded measurements would be safely assumed to contain the same value until a change was documented-especially for ventilation parameters. Therefore, we utilized last observation carried forward to fill in the values in the table between observed measures for each variable-yielding a complete dataset. As an example, this allowed us to calculate and analyze the impact of a time-weighted mean ventilator setting on ICU outcomes, which in the past was operationalized as a single ventilator setting at 10 AM in prior clinical trials and observational studies.
Discussion
The result of this method yields a long table containing a minute-by-minute record of mechanical ventilation for each subject, with all available clinical measurements present. This allows the researcher to ask a multitude of research questions that would have previously been impossible, such as exposure times to lung-protective ventilation, identifying spontaneous breathing trials, identifying adherence to site-specific clinical pathways or clinical best practices. Full SAS code and synthetic example data will be provided to attendees to allow for learning.
How presentation adds to diversity mission of the conference:
Presenter diversity: I am a first-generation college graduate. I am also a service-disabled Veteran of the United States Coast Guard.
Presentation diversity: Presentations such as this, which are hands-on and immediately applicable, are especially helpful to junior researchers, and those venturing into the world of EHR data research-particularly in an intensive care setting. The details of how to think and work through these complex data issues are often omitted from published research but are essential to understand to help move our field forward in the current era of observational data.
Presenting Author
Daniel Brinton, Medical University of South Carolina
First Author
Daniel Brinton, Medical University of South Carolina
CoAuthor(s)
Annie Simpson, Medical University of South Carolina
Andrew Goodwin, Medical University of South Carolina
Alzheimer's disease (AD) and AD-related dementias (ADRD) are neurodegenerative diseases characterized by progressive loss of cognition along with other neurobehavioral symptoms. The heterogeneity in the progression pathways from normal cognition to various intermediate stages of the disease makes it difficult to accurately diagnosis and model mathematically. This paper aims to address some of these difficulties by proposing a reliable, accurate, effective, and patient-specific prediction of risks to AD/ADRD through mining the real-world OneFlorida database collected from healthcare organizations in Florida. The development is based on a machine learning method known as conformal prediction. With this method, no model assumption is needed to obtain a valid prediction, and it is also individualized. In addition to the standard conformal prediction setting in which the prediction is verified assuming the new individual is a random sample from the whole at-risk population, we use an individualized fusion learning method to develop the second type of prediction with conditional coverage: the validity is verified among patients with the same medical condition as the target new patients. Furthermore, we provide a novel data-adaptive imputation method to handle the dependent censoring case. All the desired properties of our development are demonstrated both theoretically and in simulation studies. We also apply our method to provide reliable and individualized risk assessments for at-risk patients and supply a time frame with confidence for each individual before which the individual is unlikely to develop AD/ADRD. This assessment is clinically and economically important for health system, patients, and stakeholders.
Presenting Author
Zheshi Zheng
First Author
Zheshi Zheng
Background:
Bangladesh Maternal Mortality Survey 2016 estimated that maternal mortality ratio is 196 per 100,000 live births; this rate has remained almost unchanged in Bangladesh since 2010.With a view to reducing existing gaps and inequalities in the delivery of maternal health services, the Bangladesh government is adopting and implementing policies such as National Strategy for Maternal Health (BNSMH) 2017-2030. Thus, the overall percentage of institutional delivery has increased, but there is still variation among the districts. This study aims to identify districts with low institutional delivery and to find out socio-economic inequalities' contribution to the regional inequality of institutional deliveries.
Methods:
In this study information of 1189, 7950, and 9183 15-49-year-old women, who gave birth in the previous two years, have been used from multiple cluster surveys of 2006, 2012-13, and 2019.
To describe regional inequality on health facility delivery within unobserved sub-population (districts), growth mixture model was used. The concentration indexing measure was used to explore the socioeconomic inequality, and a Blinder-Oaxaca decomposition analysis was performed to decompose the gap among classes which was found by using growth mixture modeling approach.
Results:
The mean percentage of health facility delivery overall in Bangladesh was increasing (16 percent in 2006, 30 percent in 2012-13, and 52 percent in 2019). Several growth mixture models were fitted and AIC values showed random intercept and slope model with 3 classes (high, moderate, and low) was best. The average intercepts and slopes for each latent class indicate the trajectories vary from them, and the coefficient value was statistically highly significant among the three classes. The high, moderate, and low classes have consisted of 46, 5, and 13 districts, respectively. Facility delivery was 60 percent, 31 percent, and 16.3 for high, moderate, and low-class districts, respectively. The concentration index confirms wealth inequality in the facility delivery for all three classes. Blinder-Oaxaca decomposition indicated a significant gap in the facility delivery among the classes of the districts. The differences were 0.249, 0.415, and 0.166 for high vs moderate, high vs low, and moderate vs low, respectively. These differences were decomposed into endowments, coefficients, and interaction, and found the endowments, as well as the coefficient effect, was statistically significant. The extension part of the Blinder-Oaxaca decomposition analysis showed that education, area of residence, and utilization of antenatal care were the most significant contributors reducing the gap in the high vs low and moderate vs low latent classes, respectively (p-value <0.001). In terms of high vs moderate latent class this study found wealth status education, division, and antenatal care were the significant contributor to changing the high vs moderate gap (p-value <0.001).
Conclusions:
The result of latent trajectory analysis illustrated three different sub-groups (high, middle, and low) of districts. Districts with a lower rate of and lower increase in institutional delivery have significant wealth inequality in service consumption. Therefore, the government needs to adopt a strategy that focuses on the lowest wealth quantile of lower districts. Moreover, the decomposition results also demonstrate that the gap in educational attainment and place of residence also contribute to the significant difference in institutional delivery among the classes of districts. For a ubiquitous high institutional delivery as well as safe and improved maternal health, the government of Bangladesh should come up with a poor people-focused health strategy for the districts of low class, as well as a strategy to educate all people and close the urban-rural gap.
Presenting Author
Azizur Rahman, Department of Community Health Sciences, University of Manitoba
First Author
Md Injamul Haq Methun, Statistics Discipline, Tejgaon College
CoAuthor(s)
Md. Ismail Hossain, Department of Statistics, Jagannath University
Azizur Rahman, Department of Community Health Sciences, University of Manitoba
Md. Jakaria Habib, Department of Statistics, Jagannath University, Bangladesh
Background: Since the Affordable Care Act (ACA) was signed into law in 2010, the number of Americans without health insurance has decreased to historically low levels. The number of adults between 18 and 64 who reported being uninsured for at least part of the previous 12 months dropped from 51.0 million in 2010 to 35.1 million in 2018, largely due to expansion in Medicaid eligibility. Evaluations of Medicaid Expansion have found mostly positive outcomes, including increased health care coverage, utilization of services, and improved quality of care. Early estimates indicated that 230,000 Missourians (MO), aged 19 to 64 and earning up to 138% of the federal poverty level, would gain health care insurance as MO Medicaid expansion took effect in 2021. The current study uses interactive dashboards to characterize MO's new Medicaid enrollees and associated health outcomes. This approach to the data provides a wide-ranging view of health indicators for traditionally underserved populations and identifies health disparities.
Methods: This study examines Missouri (MO) Medicaid administrative claims data to characterize MO's Medicaid expansion enrollees during three distinct time periods (CY2017-19, CY2020-6/30/2021, 7/1/21-23). A dashboard tracks the emergent enrollment population starting with a baseline population of enrollees for calendar years 2017-2019. This study characterized recipients into six populations (children, custodial parents, elderly, disabled, pregnant women, and women's health) based on their primary medical eligibility (ME) codes for understanding demographic health indicators (i.e., age groups, geographic location, sex, and race) and their similarities or differences to the new expansion population. We chose CMS core set measures that included populations or medical conditions of interest and expressible in terms of county- and state-level rates before and after expansion.
Results: The Demographic data dashboard provides profiles of the distribution of populations eligible for Medicaid by county and over time. Map hover and tooltip features offer relevant information, including the distribution of gender and race for that county. The Core Set Measures dashboard visualizes the distribution of rates for outcomes by county and across time. Selecting counties on the map will add that county's trend line to the trend line chart, allowing county-to-county and county-to-state comparisons over time for each measure.
Conclusion: Interactive dashboards are useful for characterizing and analyzing Medicaid population demographics, health status, and health care usage. The use of quality health indicators can increase understanding of health disparities and reveal areas for intervention. These population health visualizations inform health education, policies, and practice and contribute to an understanding of whether and how expansion improves Missourians' health.
Presenting Author
Tracy Greever-Rice, University of Missouri Center for Health Policy, University of Missouri
First Author
Tracy Greever-Rice, University of Missouri Center for Health Policy, University of Missouri
CoAuthor
Shannon Canfield, The Center for Health Policy and The Department of Family Community Medicine, School of Medicine, Un
Chronic obstructive pulmonary disease (COPD) affects hundreds of millions of people in the world. Current evidence suggests that using conventional models (logistic regression with structured data) for predicting COPD readmissions have moderate performance. Increased adoption of EHR systems from health providers over the last years has resulted in the accumulation of valuable information which can be used to guide health care policy. Electronic Health Records (EHRs) are electronic records of patients' health information, including structured data stored in tabular form such as laboratory test results and demographics, and unstructured data in the form of clinical notes and reports. Clinical research investigators commonly analyze structured EHR data to gain the insight necessary to inform medical professionals and guide public health policymakers. However, a wealth of potentially useful information about patients' clinical history, stored in the form of free-text clinical notes, remains underutilize. Our objective is to use NLP to harness the additional information contained in clinical notes to improve prediction of COPD 30-day readmission. Our sample included 1670 patients at least 40 years old, with an inpatient visit at our institution from 2010 to 2019 for any reason with a diagnosis for COPD. Patient's age, gender, race, primary payer, length of stay, discharge disposition, and comorbidities were the covariates which formed the structured data while physician's discharged notes were processed with NLP. A logistic regression model and a neural network model for classification produced a ROC AUC of 58% and 59% respectively. A Bidirectional Encoder Representations from Transformers (BERT) NLP framework model was training on discharge notes using a ratio of 50:30:20 for train, validation and test datasets. The BERT model resulted in a AUC of 59%. Finally, a neural network for the structured data and a BERT model for the unstructured data were nested within a multimodal neural network. The output of the encoding layers of the two sub-models were concatenated before being forwarded in the final layer of the model for classification. The multimodal model produced an AUC of 63%. Our results suggests that additional improvement in prediction accuracy can likely be gained by utilizing patients' health information stored in both structured and unstructured forms.
Presenting Author
Ioannis Malagaris
First Author
Ioannis Malagaris
CoAuthor(s)
Efstathia Polychronopoulou, UTMB
Yong-Fang Kuo, University of Texas Medical Branch
Duarte Duarte, University of Texas Medical Branch
The projection of bipartite networks of patients linked to physicians to a shared-patient physician network allows researchers to study the way the physician ecosystem influences health services. Such networks can be constructed from insurance claims such as in Medicare data or from electronic medical records, etc. Previous research has investigated how these networks associate with patient care, outcomes, as well as research trial outcomes. In this work, we investigate whether the structure of physician-physician networks contains markers that associate with a physician's willingness to participate in a research trial.
At 34 select hospitals across the country, 349 hospitalists associated with Sound Physicians medical working group were asked to participate in clinical research. Of those, 164 (46.99%) agreed to participate. Participants were invited using a step-wedge trial design, between September 1 of 2020 and February 1 of 2021. We identified Medicare claims for 305 (87.39%) of the invited physicians and constructed a unipartite physician network connecting physicians with 15 or more shared patients in 2019.
Due to the COVID-19 pandemic, waves of high-infection rates across the country caused massive disruption to hospital systems and infrastructure.
We hypothesized that physician's movement between hospitals would increase during this period. In addition to the above physician network, we also constructed networks where nodes are hospitals and are connected if they have at least one physician billing at both hospitals. Hence, in this work, we propose a multi-level network methodology which considers both the physician shared-patient network as well as a hospital level shared-physician network.
Networks were built for each month between December 1, 2018 and May 31, 2021 using patient encounters provided by Sound Physicians. We defined the start of pandemic months as March 1, 2020. We found that the during pandemic, hospital networks were more densely connected (p=0.041) and had smaller diameter (p=0.006) reflecting the increase in the extent to which physicians practiced at multiple hospitals.
This dual increase of physician-sharing between hospitals and of physicians practicing at multiple hospitals increases the possibility that hospital-specific attitudes towards research participation may diffuse to hospitals with physician overlap. In ongoing work, we are evaluating the extent to which both physician-level and hospital-level network factors associated with likelihood of trial participation.
The social network of physician colleagues both with hospitals and across hospitals may influence their willingness to participate in research trials. Further study of this phenomenon from a social network perspective may provide insights on how to leverage physician-physician networks to encourage higher rates of research participation.
Presenting Author
Carly Bobak
First Author
Carly Bobak
CoAuthor
James O'Malley, Dartmouth University, Geisel School of Medicine
Under rolling enrollment, individuals or entities choose when to enroll in an intervention. This staggered enrollment pattern presents challenges for a rigorous impact evaluation and can introduce bias if the evaluation design does not account for selection pressures that may lead specific entities to enroll at specific times. In this talk, I describe solutions to some of the common challenges, focusing on comparison group selection and the optimal matching tool GroupMatch. As an example, we will look at matching results from the evaluation of the Medicare Care Choices Model, which provides supportive and palliative services to Medicare beneficiaries at the end of life.
Presenting Author
Jonathan Gellar, Mathematica Policy Research
First Author
Jonathan Gellar, Mathematica Policy Research
Qatar being a vibrant nation, is always abreast in the developmental initiatives to make the country on par with global standards. Qatar National Vision 2030 (QNV 2030) has envisioned a healthy and prosperous through National Development Strategies as well as National Health Strategies. Health being given a high priority in the agenda of Government; national health strategies are deployed to accelerate the reach of healthcare services and to work as an anchor for multifaceted health development in the country. Thus, the overarching NHS2 coupled with timely policy frameworks could successfully combat the impact of the pandemic in the country. However, there is a felt need to observe the impact of changing scenarios (like pandemic) on the care given to vulnerable groups like people suffering with physical and mental health challenges. Having understood this priority, present research work aims to share the strides that the country is taking in addressing the demand-supply gaps in the state-of-the-art healthcare delivery system, particularly to meet the needs of vulnerable people of the society. Also, the research work invites a dialogue as well as insightful inputs from the global audience to overcome the mental health challenges in long-term perspective.
Presenting Author
Badria Alharmi, Planning and Statistics Authority
First Author
Badria Alharmi, Planning and Statistics Authority
Background: As mobile phones are widely available, health providers and patients can communicate directly through SMS text messages and phone calls to support overcoming barriers to adherence to TB medication. However, there is conflicting evidence regarding the efficacy of SMS texts to promote TB medication adherence. Additionally, most mobile-based adherence interventions are not designed in theory based. We developed a mobile-assisted medication adherence support (Ma-MAS) intervention using the medical research council (MRC) framework based in a local context targeting audience needs and the effectiveness of this intervention evaluated in a sample of TB patients in Addis Ababa, Ethiopia.
Methods: A parallel group randomized control trial design was used to evaluate the effectiveness of Ma-MAS intervention. In total, 186 adult TB patients (93 per group) were randomly assigned 1:1 to one of the two groups using a computer-generated algorithm. Participants in the Ma-MAS group received daily SMS texts and weekly phone calls regarding their daily medication intake and reminders to attend clinic visits for 8 weeks. Participants in the control group did not receive SMS texts or phone calls but received the same routine standard care as the Ma-MAS group. All participants were followed for 8 weeks during intervention period at the continuation phase of treatment. The primary outcome was the proportion of adherence measured by urine tests for isoniazid (INH) metabolites at the end of the 4th and 8th week of follow-up period. The outcome assessors were blinded to participants group assignment. Analysis of a multivariable binomial generalized linear model was employed to evaluate the effect of Ma-MAS at a significance level of (P value) < 0.05.
Results: Ma-MAS significantly improved adherence to TB medications by 15.25% (95%CI: 5.38, 25.12; P-value=0.0065) after 8 weeks of intervention compared to the standard care alone in the control group. At the end of the 8 weeks follow-up period, the predicted probability of adherence to TB medication in the Ma-MAS group was 86% (95%CI: 81, 93), and in the control group was 70% (95%CI: 61, 79). Ma-MAS also improved adherence to TB medication by 15.30% (95%CI: 6.68, 23.90; P-value=0.0022) after 4 weeks of intervention compared with the control group.
Conclusion Ma-MAS intervention based on information-motivation-behavioural skills model and behavioural change techniques is useful to improve adherence to TB medication.
Funding: The research is partially funded by Flinders University and supported by the Fogarty International Center and of the National Institutes of Health under Award Number D43TW009127. The funder and sponsor had no role in design, implementation, analysis, and interpretation of the findings. All content is the responsibility of the authors and does not necessarily represent the official views of the Flinders University or National Institutes of Health.
Speaker
Zekariyas Sahile Nezenega, Flinders University