Simulation and Modeling for Improving Access to Care for Underserved Populations

Indiana University-Purdue University Indianapolis (IUPUI)This research, through partnership with seven Community Health Centers (CHCs) in Indiana, constructed effective outpatient appointment scheduling systems by determining care needs of CHC patients, designing an infrastructure for meaningful use of patient health records and clinic operational data, and developing prediction and simulation models for improving access to care for underserved populations. The aims of this study are 1) redesigning appointment scheduling templates based on patient characteristics, diagnoses, and clinic capacities in underserved populations; 2) utilizing predictive modeling to improve understanding the complexity of appointment adherence in underserved populations; and 3) developing simulation models with complex data to guide operational decision-making in community health centers. This research addresses its aims by applying a multi-method approach from different disciplines, such as statistics, industrial engineering, computer science, health informatics, and social sciences. First, a novel method was developed to use Electronic Health Record (EHR) data for better understanding appointment needs of the target populations based on their characteristics and reasons for seeking health, which helped simplify, improve, and redesign current appointment type and duration models. Second, comprehensive and informative predictive models were developed to better understand appointment non-adherence in community health centers. Logistic Regression, Naïve Bayes Classifier, and Artificial Neural Network found factors contributing to patient no-show. Predictors of appointment non-adherence might be used by outpatient clinics to design interventions reducing overall clinic no-show rates. Third, a simulation model was developed to assess and simulate scheduling systems in CHCs, and necessary steps to extract information for simulation modeling of scheduling systems in CHCs are described. Agent-Based Models were built in AnyLogic to test different scenarios of scheduling methods, and to identify how these scenarios could impact clinic access performance. This research potentially improves well-being of and care quality and timeliness for uninsured, underinsured, and underserved patients, and it helps clinics predict appointment no-shows and ensures scheduling systems are capable of properly meeting the populations’ care needs.2021-12-2

Redefining Existing Concrete Compressive Strength Acceptance Standard in Iran Concrete Code (ABA), by Experimental Data

In Iran Concrete Code (ABA), the criteria for calculation of standard deviation (s) are comprehensive and holistic. However, if it would be determined separately for each geographical area, significant changes could occur due to the use of concrete as one of the common materials. This paper analyses the criteria and redefines the acceptance standards for concrete compressive strength in ABA using experimental data available in Kohgiluyeh and Boyer-Ahmad and Fars provinces. The main hypothesis of the study is that using the statistical analysis of the test specimens for three categories C21, C30 and C35 in various projects located in Kohgiluyeh and Boyer-Ahmad and Fars provinces, extracting standard deviations, mean and the compressive strength of the specimens and their comparison with ABA proposed relationships and values, it is possible to propose new amendments for these areas in line with economic savings in national and international projects. In this study using the quantitative Strategy, library - Internet studies, field studies and in cooperation with the concrete labs, required information for 4878 concrete specimens was collected from the above-mentioned areas. By analysing the acceptance regulations for the specimens based on ABA and comparing the standard deviation of these data with the formulas of the regulations, significant results were obtained for the standard deviation factor correction and finally some formulas were suggested for the acceptance of the concrete specimens

Antibacterial activity of indium curcumin and indium diacetylcurcumin

Studies on curcumin, the principal element of turmeric powder, have demonstrated several biological actions such as antibacterial activity. Evaluation of new analogs or new compounds of curcumin for their antibacterial effect is interesting for researchers. In this in vitro study, we attempted to test the antibacterial activity of indium curcumin (In(CUR)3), indium diacetylcurcumin (In(DAC)3), and diacetylcurcumin (DAC) in comparison with curcumin. The action of these agents were examined on Staphylococcus aureus (ATCC 25923), Staphylococcus epidermidis (ATCC 14990), Pseudomonas aeruginosa (ATCC 27853), and Escerichia coli (ATCC 25922). Curcumin was effective against S. aureus and S. epidermidis, whereas In(DAC)3 showed activity against S. epidermidis and P. aeruginosa. The effect of In(DAC)3 on P. aeruginosa is an advantage. Strikingly, In(CUR)3 exhibited antibacterial activity on all the four mentioned strains. DAC did not show antibacterial effect on any of the four test bacteria. The minimum inhibitory concentration (MIC) of curcumin was 187.5 μg/ml for S. aureus, and 46.9 μg/ml for S. epidermidis. However, the MIC of In(CUR)3 was lower for the same bacterial strains (93.8 μg/ml for S.aureus and 23.4 μg/ml for S. epidermidis). Therefore, In(CUR)3 was found to have more antibacterial effect than curcumin itself and could be a suitable candidate for further in vivo investigations

Effect of modified starch used alone or in combination with wheat flour on the sensory characteristics of beef sausage

  The effects of modified waxy maize starch (MWMS) (1–3.25%) on the sensory characteristics of 60% beef sausages were investigated by replacing the varying levels of sunflower oil or both sunflower oil and wheat flour (WF). The addition of MWMS improved the red color, the palatability, and the overall acceptability compared to the control sausages. The Color was medially and positively correlated with firmness. The correlations between color and taste and between taste with juiciness and firmness were weak and positive. The significant correlations were not observed between palatability and overall acceptability and with the other sensory characteristics. Juiciness was negatively correlated with firmness but was not significantly different. As a result, MWMS offset the effects of lowering the fat content.  

The Granted Effects of Agricultural Bank Credits on Total Factor Productivity in Agriculture Production

The present study examined the effect of agricultural bank credit on the productivity of production factors in the agricultural sector over the period 1971-2012 using the Auto Regression pattern with wide interruptions. Solow residual model has been used to calculate the growth rate of total factor productivity of agricultural sector. The results showed that credit variable in the both long-term and short-term has a positive effect on total production factors productivity in the agricultural sectors of Iran. Therefore, an increase in credits granted to the agricultural sector has caused to enhance the growth of these sectors and increase total productivity of production factors in the agricultural sector. The effect of energy consumption, exports of agricultural sector, research and development expenditures in the agricultural sector are also positive on total productivity of production factor in the short and long term. But, in the long run, impact of liquidity and oil income on total productivity production factor in the agricultural sector is negative. Therefore, planning in this regard is important

Prevalence of Epilepsy in Iran: A Meta-Analysis and Systematic Review

How to Cite This Article: Sayemiri K, Tavan H, Sayemiri F, Mohammadi I, Carson KV. Prevalence of Epilepsy in Iran : A Meta-Analysis and Systematic Review. Iran J Child Neurol. 2014 Autumn; 8(4):9-17.AbstractObjectiveEpilepsy is one of the most common diseases in Iran contributing to an array of health problems. In light of this, the aim of the present study is to examine the prevalence of epilepsy in Iran through a systematic review and meta-analysis.Materials & MethodsA systematic search of several databases including PubMed, scientific information databases, Google, Google scholar, Elsevier and Scopus was conducted in June 2013. Observational studies were considered for inclusion ifthey were published in Iranian and examined epilepsy prevalence and/or related risk factors. Meta-analysis was conducted using a random effect model with the DerSimonian/Laird method. Heterogeneity was examined using the Breslow- Day test and inconsistency using the I2 statistic.ResultsA total of 45 studies were identified from the search strategy. Of these, nine published manuscripts with a total of 7,723 participants were included within the review. The pooled prevalence of epilepsy in Iran was estimated to be around 5% (95% confident interval (CI) 2 to 8). For each region the prevalence of epilepsy in central, northern and eastern Iran were 5% (95%CI 2 to 8), 1% (95%CI -1 to 3) and 4% (95%CI 3 to 11) respectively. The most common risk factors in order of prevalence were somatic diseases 39% (95%CI 15 to 62),convulsion 38% (95%CI 11 to 65), mental diseases 36% (95%CI 15 to 95) and hereditary development 26% (95%CI 9 to 42). A meta-regression model identified a declining trend in the prevalence of epilepsy within Iran for the last decade.ConclusionPooled analyses from the nine included publications in this review estimate the prevalence of epilepsy in Iran to be around 5%. Although this result is much higher than rates in other countries, a declining trend in prevalence over the pastdecade was also identified. References1. Valizadeh L, Barzegar M, Akbarbegloo M, ZamanzadehV, Rahiminia E, Ferguson CF. The relationship between psychosocial care and attitudes toward illness in adolescents with epilepsy. Epilepsy and Behavior 2013;27: 267–271.2. Widera E, LikusW, Kazek B, Niemiec P, Balcerzyk A,Aleksander L, Siero N, gak I. CYP3A5 * 3 and C3435TMDR1 Polymorphisms in Prognostication of Drug-Resistant Epilepsy in Children and Adolescents. Hindawi Publishing Corporation Biomed Research International 2013;12: 7 -10.3. Koochaki E, Daneshvar R. Evaluation of Seizure Attacksin Patients with Cerebrovascular Accident. Zahedan J ResMed Sci 2013; 15: 29-32.4. Asadi-PooyaA, Sharifzade M. Lennox–Gastaut syndrome in south Iran: Electro-clinical manifestations. Seizure 2012; 21: 760-763.5. Motamedi M, Sahraian M, Moshirzadeh S. A Cross Sectional Study Evaluating Perceived Impact of Epilepsy on Aspects of Life. Zahedan J Res Med Sci 2012; 14: 33-366. Scott RA, Lhatoo SD, Sander J. The treatment of epilepsy in developing countries: where do we go from here? Bull WHO 2001;79:344–345.7. Bharucha, N.E. Epidemiology of epilepsy in India. Epilepsia 2003;44: 9-11.8. Ronnie D. Horner.Racial/ethnic disparities in the treatment of epilepsy: What do we know? What do we need to know? Epilepsy & Behavior 2006; 9: 243–264.9. Nachvak M, Haghighat HR, Rezaei M. Prevalence and monitoring of retarded Childs in Tehran at 2002. Quarterly of science-research journal of Kermanshah University of Medical Sciences.2004; 3: 34-42.10. Etemadifar M, Mirabdolbaghe P. Demographic and clinical characteristics of young epilepsy mortalities in Isfahan. Two quarterly of south pediatric, Persian golf center of health researches in Boushehr University of Medical Sciences. 2005; 2: 160-164.11. Najafi MR, Rezaei F, Vakili Zarch N, Dehghani F, Barakatein M. Survey of pattern of personality and psychopathology in patients with Grandmal and complexpartial epilepsy and comparison with control group. Journal of Shaheed Sadoughi University of Medical Sciences. 2010; 2:84-91.12. Pashapour A, Sadrodini A. Grandmal epilepsy and EEG variations in primary school children at Tabriz. MedicalJournal of Tabriz University of Medical Sciences. 2001; 50: 23- 27.13. Mohammadi M, Ghanizadeh A, Davidian H, MohammadiM, Norouzian M. Prevalence of epilepsy and co morbidity of psychiatric disorders in Iran. Seizure. 2006; 15: 476-482.14. Nasehi M.M, Mahvalati Shamsabadi F, Ghofrani M. Associated Factors in Response to Treatment in Childrenwith Refractory Epilepsy. J Babol University Med Sci (4):2010; 12: 61-66.15. Kaheni S, Riyasi HR, Rezvani Kharashad MR, Sharifzadeh Gh, Nakhaei S. Prevalence of epilepsy in children at primary schools and awareness of teachers about epilepsy at primary schools of Birjand at 2010. Novel cares, Quarterly of science journal of nursing and midwifery in Birjand University of Medical Sciences. 2011; 3:135-142.16. Rezaei AK, Saeidi Sh. Survey of starting age and genderof epilepsy and effective parameters on the Sina and Ghaem hospitals patients at 1989 till 1995. Rehabilitation magazine. 2000; 2: 52-57.

Data Analytics and Modeling for Appointment No-show in Community Health Centers

Objectives: Using predictive modeling techniques, we developed and compared appointment no-show prediction models to better understand appointment adherence in underserved populations. Methods and Materials: We collected electronic health record (EHR) data and appointment data including patient, provider and clinical visit characteristics over a 3-year period. All patient data came from an urban system of community health centers (CHCs) with 10 facilities. We sought to identify critical variables through logistic regression, artificial neural network, and naïve Bayes classifier models to predict missed appointments. We used 10-fold cross-validation to assess the models’ ability to identify patients missing their appointments. Results: Following data preprocessing and cleaning, the final dataset included 73811 unique appointments with 12,392 missed appointments. Predictors of missed appointments versus attended appointments included lead time (time between scheduling and the appointment), patient prior missed appointments, cell phone ownership, tobacco use and the number of days since last appointment. Models had a relatively high area under the curve for all 3 models (e.g., 0.86 for naïve Bayes classifier). Discussion: Patient appointment adherence varies across clinics within a healthcare system. Data analytics results demonstrate the value of existing clinical and operational data to address important operational and management issues. Conclusion: EHR data including patient and scheduling information predicted the missed appointments of underserved populations in urban CHCs. Our application of predictive modeling techniques helped prioritize the design and implementation of interventions that may improve efficiency in community health centers for more timely access to care. CHCs would benefit from investing in the technical resources needed to make these data readily available as a means to inform important operational and policy questions

Predictive Modeling for Appointment No-show in Community Health Centers

Reducing no-show rates is one of the most important measures of access to care in Community Health Centers (CHCs). We used EMR and scheduling data to develop no-show prediction models to help design effective scheduling processes and system redesign for greater access in CHCs. Patient and provider characteristics and visit features are key elements for predicting patient adherence with an appointment