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5.9 49.6 Cancer 17.0 83.0 0.01 No Cancer 21.0 79.0 Weak/Failing Kidneys 25.9 74.1 0.01 No Weak/Failing Kidneys 20.1 79.9 Stroke 15.1 84.9 0.01 No Stroke 21.0 79.0 Heart Failure 13.3 86.7 0.05 No Heart Failure 17.5 82.5 Table 2 Ratio of hepatitis B vaccination rates among adult individuals with diabetes by demographics. Odds Ratio 95% Confidence Interval p-value Asians Unadjusted 1.46 1.36–1.57 0.01 Blacks Unadjusted 1.31 1.19–1.44 0.05 Whites (ref) 1.00 1.00 n/a Asians Adjusted 1.36 1.29–1.43 0.01 Blacks Adjusted 1.36 1.10–1.68 0.01 Whites (ref) 1.00 1.00 n/a Hispanic 0.80 0.26–2.42 0.69 Female 1.69 1.33–2.15 0.01 Age 41–60 years (ref 19–40 years) 0.46 0.41–0.51 0.01 Foreign Born 1.33 0.54–3.44 0.56 Married/living partner 1.21 0.93–1.56 0.15 Lack of College Degree 0.63 0.53–0.74 0.01 Alcohol 1.34 1.16–1.55 0.01 Overweight 0.81 0.31–2.11 0.44 Obese 0.94 0.35–2.49 0.86 Normal weight (reference) 1.0 1.0 n/a Have Insurance 1.19 0.68–2.09 0.53 Cancer 0.92 0.91–0.94 0.01 Weak/Failing Kidneys 1.562 1.43–1.70 0.01 Stroke 0.18 0.15–0.21 0.01 Heart Failure 1.28 0.95–1.70 0.10

Introduction Recent evidence show that hepatitis B infection occurs more frequently among individuals with type 2 diabetes than the general population [1–3]. The odds for developing acute hepatitis B for individuals with diabetes is said to be as much as twice that for non-diabetics. The higher rates may not only be related to traditional sources of exposure such as blood transfusion, unsafe sex, unsterile surgery, intravenous drug abuse or other conditions involving improper handling of body fluids. From several outbreaks, hepatitis B infection among diabetics has also shown to be very often associated with poor hygiene standards surrounding the use of blood glucose monitoring equipment such as glucometers, lancets, or other diabetes-care equipment such as syringes and insulin pens either by patient self-administration or by caregiver administration at home, residential facilities, or hospitals [1,4]. The annual costs of managing type-2 diabetes and diabetes related illnesses is burdensome [5]. Superimposed hepatitis infection would substantially increase the burden on healthcare resources. The vaccination of all adults with diabetes aged 19–60 years is said to be moderately cost-effective, and forms the basis of CDC recommendations for this cohort [6–8]. However, it is unclear at present time of what the value of hepatitis B vaccination for diabetic individuals sixty years or older.

healthcare resources. The vaccination of all adults with diabetes aged 19–60 years is said to be moderately cost-effective, and forms the basis of CDC recommendations for this cohort [6–8]. However, it is unclear at present time of what the value of hepatitis B vaccination for diabetic individuals sixty years or older. Some epidemiological studies suggest social factors including racial, knowledge, and geographic disparities; poverty, and inadequate access to health care have helped sustain the morbidity surrounding chronic hepatitis B infections [9–11]. The prevalence of hepatitis B infection is disproportionately high among Asian and African American populations [12,13]. The aim of the study is to compare the rates of hepatitis B vaccination among racial, demographic categories. A secondary objective was to evaluate the hepatitis B vaccination rates for various co-morbid conditions.

Some epidemiological studies suggest social factors including racial, knowledge, and geographic disparities; poverty, and inadequate access to health care have helped sustain the morbidity surrounding chronic hepatitis B infections [9–11]. The prevalence of hepatitis B infection is disproportionately high among Asian and African American populations [12,13]. The aim of the study is to compare the rates of hepatitis B vaccination among racial, demographic categories. A secondary objective was to evaluate the hepatitis B vaccination rates for various co-morbid conditions. Methods Data Sources: Data for the study was taken from the 2000–2013 National Health Interview Survey (NHIS). The NHIS is a multipurpose national health survey conducted by the National Center for Health Statistics (NCHS) at the Centers for Disease Control and Prevention (CDC) and is designed to provide information about a wide range of health topics for the non-institutionalized US household population. Data was collected by trained interviewers with the U.S. Census Bureau who administer the survey during visits to selected households and by phone interview to administer the survey. The survey uses multistage, cluster sampling, with weights that are representative of the US adult population. All analyses performed in this study utilized weighted statistics based on the final weights provided with the NHIS data sets. These weights represent a product of weights for corresponding units computed in each of the sampling stages. Details on sample design can be found in Design and Estimation for the National Health Interview Survey 1995–2005, 2006–2013 [14].

eighted statistics based on the final weights provided with the NHIS data sets. These weights represent a product of weights for corresponding units computed in each of the sampling stages. Details on sample design can be found in Design and Estimation for the National Health Interview Survey 1995–2005, 2006–2013 [14]. The main factor evaluated in the study was hepatitis vaccination. Survey participants (SPs) gave answers to the survey question “Have you EVER received the hepatitis B vaccine?” Answers were recorded in a binary form as yes or no. Diabetes was categorized as yes or no based on self-reports. Age was recorded in whole numbers from 0–120 years as of their last birthday. The study focused on adults of 20–59 years. Age was further categorized into 20–39 years, and 40–59 years. Differences in composition and distribution of categorical and continuous were assessed. χ2 test was employed to assess differences in categorical variables. A logistic regression model was used to determine associations between hepatitis B vaccine use and racial groups, demographic characteristics and co-morbid medical conditions among diabetics. The PROC SURVEY procedure was utilized to account for multistage sampling and weights. All analysis was performed in SAS 9.3 [15].

logistic regression model was used to determine associations between hepatitis B vaccine use and racial groups, demographic characteristics and co-morbid medical conditions among diabetics. The PROC SURVEY procedure was utilized to account for multistage sampling and weights. All analysis was performed in SAS 9.3 [15]. Results A total of 36, 489 adults with diabetes aged 19–60 years were surveyed and provided valid data for the analysis. 50% of participants were female, 79% were of white race, 16.4% were of black race, and 3.9% were of Asians race (p<0.05); mean age (±SEM) was 45.7 years (±0.02). The overall rate of hepatitis B vaccination among adults 19–60 years old (population in whom Hepatitis B vaccination is recommended) was 20.2%. The rate of vaccination differed across racial groups (Asians 26.0% vs. blacks 23.9%; and whites 19.4%; p<0.01). Diabetics at least 40 years and older (25.3% vs. 40.4%), who did not graduate college (19.0% vs. 27.8%), or were foreign born (19.3% vs. 20.6%) also had lower vaccination rates than their corresponding groups (Table 1). In addition, individuals without health insurance coverage had lower vaccination rates (20.1% vs. 24.0%); individuals with stroke had very low vaccination rates (15.1% vs. 21.0%, p<0.01), while individuals with failing kidneys had high vaccination rates (25.9% vs. 20.1%, p<0.01). The unadjusted odds ratio (OR) for hepatitis B vaccination was 1.46 (95% CI=1.36–1.57, p<0.01) for diabetic Asians, and 1.31 (95% CI=1.19–1.44, p<0.01) for diabetic blacks; (Table 2); both when compared to diabetic Whites. Multivariate logistic regression showed that the adjusted (for age, BMI and college education) OR for hepatitis B vaccination was 1.36 (95% CI=1.29–1.43, p<0.01) for diabetic Asians, and 1.36 (95% CI=1.19–1.44, p<0.10) for diabetic blacks; both when compared to diabetic Whites.

); both when compared to diabetic Whites. Multivariate logistic regression showed that the adjusted (for age, BMI and college education) OR for hepatitis B vaccination was 1.36 (95% CI=1.29–1.43, p<0.01) for diabetic Asians, and 1.36 (95% CI=1.19–1.44, p<0.10) for diabetic blacks; both when compared to diabetic Whites. Conclusion The higher hepatitis B vaccine utilization was more marked among Asians, and Blacks, when compared to Whites. The disparity in coverage was more substantial for individuals who were non-citizens, recent immigrants, or non-English speakers. Our data showed that Hispanics, and foreign-born individuals also had lower vaccination rates. Additionally, individuals without a college level degree or health insurance coverage also had lower rates of utilization.

as more substantial for individuals who were non-citizens, recent immigrants, or non-English speakers. Our data showed that Hispanics, and foreign-born individuals also had lower vaccination rates. Additionally, individuals without a college level degree or health insurance coverage also had lower rates of utilization. Discussion Our results show low hepatitis B vaccination rates among patients enrolled in the study, despite CDC recommendation for vaccination for all adult patients under age 60 [16]. Furthermore, vaccination rates for individuals with diabetes differ among racial, demographic, medically co-morbid categories. The racial disparity in hepatitis B vaccination rate seen in our study may be consistent with the historically high hepatitis B prevalence recorded in the Asian and African American populations, predisposing to relative cultural awareness of their risk profile. Additionally, physicians may be more vigilant in monitoring the aforementioned populations due their increased risk of hepatitis B, thereby increasing the vaccination rates compared to other populations. However, further vigilance is necessary on the part of health-care workers for promoting Hepatitis B vaccine coverage, as coverage rates fall far short of the blanket recommendation for vaccination. Culturally sensitive vaccination programs have been previously effective in increasing vaccination rates amongst high risk patient populations, and may need to be implemented to improve adherence to vaccination recommendations amongst patients with diabetes. For example, a past CDC survey showed hepatitis B vaccine coverage to be 41%–61% and 2%–11% for cities with and without vaccination programs for Asian Pacific Islander children, respectively [16].

lations, and may need to be implemented to improve adherence to vaccination recommendations amongst patients with diabetes. For example, a past CDC survey showed hepatitis B vaccine coverage to be 41%–61% and 2%–11% for cities with and without vaccination programs for Asian Pacific Islander children, respectively [16]. Lu et al., showed lower hepatitis B vaccination coverage rates among foreign born individuals compared to those who were US born [17]. This is consistent with the findings of our study. This may be due in part to economic emigrational forces and globalization affecting highly endemic regions of Southeast and Far East Asia [18]. PJ and colleagues also recognized the need for more culturally sensitive educational outreach efforts which promote awareness for HBV screening, prevention, and treatment within a community with high foreign born non English speakers [17]. PJ suggests the delivery of health care in native languages and increased awareness in the primary care community-based clinics as strategies which can be implemented to improve vaccination rates. Our data shows college education and health insurance status, commonly used surrogates for socioeconomic status, were correlated with vaccination utilization. This is consistent with other studies that have shown differences in hepatitis B vaccination status related to socioeconomic status. Namely, important factors affecting hepatitis B coverage include household income level, education, older age and access to care [19,20].

us, were correlated with vaccination utilization. This is consistent with other studies that have shown differences in hepatitis B vaccination status related to socioeconomic status. Namely, important factors affecting hepatitis B coverage include household income level, education, older age and access to care [19,20]. Finally, it is important to note that the CDC recommendations [21], for vaccinations of adults with diabetes had been reaffirmed by the most recently published 2017 Standards of Medical Care in Adults with Diabetes by the American Diabetes Association [22]. We also like to mention that the lower rate of vaccination among whites, and the higher rates of vaccination among obese could be explained by the higher rate of co-morbidity and disease severity index among minorities and obese patients bringing these individuals more frequently to medical attention and leading to more opportunities for vaccinations. Funding This work is sponsored in part by the Brooklyn Health Disparities Center NIH grant #P20 MD006875. Table 1 Hepatitis B vaccine rates among ethnic and demographic groups of Adult Diabetic individuals.

We also like to mention that the lower rate of vaccination among whites, and the higher rates of vaccination among obese could be explained by the higher rate of co-morbidity and disease severity index among minorities and obese patients bringing these individuals more frequently to medical attention and leading to more opportunities for vaccinations. Funding This work is sponsored in part by the Brooklyn Health Disparities Center NIH grant #P20 MD006875. Table 1 Hepatitis B vaccine rates among ethnic and demographic groups of Adult Diabetic individuals. Vaccination (%) No Vaccination (%) p-value All survey participants 28.6 71.4 0.01 Asians 26.0 74.0 0.01 Blacks 23.9 76.1 Whites 19.4 81.6 Hispanic 19.0 81.0 0.01 Non-Hispanic 20.6 79.4 Female 22.1 77.9 0.01 Male 18.8 81.2 Age 19–39 years 40.4 59.6 0.01 Age 40–60 years 25.3 74.7 Foreign Born 20.6 79.4 0.01 US Born 80.7 19.3 No College Degree 19.0 81.0 College Degree 27.8 72.2 0.01 Current/Former Alcohol use 29.2 70.8 0.01 Lifetime abstainers 23.4 76.6 No Insurance 24.0 76.0 0.01 Insurance 20.1 79.9 Normal weight (BMI 18.5–24.99) 14.5 17.5 0.01 Overweight (BMI 25–29.99) 29.6 32.9 Obese (BMI ≥ 30) 55.9 49.6 Cancer 17.0 83.0 0.01 No Cancer 21.0 79.0 Weak/Failing Kidneys 25.9 74.1 0.01 No Weak/Failing Kidneys 20.1 79.9 Stroke 15.1 84.9 0.01 No Stroke 21.0 79.0 Heart Failure 13.3 86.7 0.05 No Heart Failure 17.5 82.5 Table 2 Ratio of hepatitis B vaccination rates among adult individuals with diabetes by demographics.

Introduction Diabetes is a major public health issue affecting millions of people in the United States and worldwide. In the United States alone, about 30.3 million people (9.4% of the population) have diabetes [1]. Worldwide, a staggering 422 million have diabetes according to 2014 estimates [2]. Diabetes complications over years of exposure include cardiovascular disease -- stroke and myocardial infarction -- and microvascular disorders -- kidney failure, blindness and amputation [3]. Acute and potentially fatal complications of diabetes include hyperglycemic crises, specifically diabetic ketoacidosis (DKA) and hyperglycemic hyperosmolar state (HHS) [4]. While overall in-hospital case-fatality rates for hyperglycemic crises have been on the decline in the United States (current estimates of 0.4% mortality rate of DKA) [5,6], HHS still has an unacceptably high mortality rate (ten times that of DKA) [7]. Furthermore, certain geographic locales and certain races have an unacceptably high mortality rate. For example, in one study of 270 patients in India, the mortality rate of DKA was 30% [8]. In a rural regional hospital in South Africa, the mortality rate of DKA was 17.14% [9]. Even within the United States, between 2012–2014, the total number of deaths from diabetes among black children was over twice that of their white counterparts [10]. This is even though incidence and prevalence of diabetes in black children is lower than for their white counterparts [11,12].

rate of DKA was 17.14% [9]. Even within the United States, between 2012–2014, the total number of deaths from diabetes among black children was over twice that of their white counterparts [10]. This is even though incidence and prevalence of diabetes in black children is lower than for their white counterparts [11,12]. The disparity among different regions and races may be secondary to differences in access to resources, including accessible, up to date clinical guidelines. Despite the wide availability of the American Diabetes Association (ADA) clinical practice guidelines for patients with hyperglycemic crises [13,14] and those of the American Association for Clinical Endocrinologists (AACE) [15], these guidelines do not appear to adequately improve survival among all populations with hyperglycemic crisis. Indeed, the utility of clinical guidelines is only in their practical implementation [16–18]. Even with clinical practice guidelines available, the time, effort and skills needed to access these guidelines are not available to everyone [19].

o adequately improve survival among all populations with hyperglycemic crisis. Indeed, the utility of clinical guidelines is only in their practical implementation [16–18]. Even with clinical practice guidelines available, the time, effort and skills needed to access these guidelines are not available to everyone [19]. In this editorial, we introduce a new type of clinical decision support (CDS) tool for DKA/HHS, which provides clinicians a subset of the DKA/HHS guidelines personalized to the clinical features at the point-of-care. In contrast to existing CDS tools that automate decisions or order sets [20,21], we intended that the tool helps clinicians make informed decisions rather than make passive decisions. This allows the clinicians to benefit from individualized education and eventually become independent of the electronic tools. Our app has the potential of improving the mortality of DKA and HHS across all locations and races. Framework design: Interactive guideline with participatory design Our team built an interactive text-based platform in which each paragraph, sentence or summary item is activated or deactivated by triggers based on user inputs. The user chooses among button choices and inputs numeric values on the first screen, and the inputs determine the activation of the triggers. (Figure 1-Left Panel)

eam built an interactive text-based platform in which each paragraph, sentence or summary item is activated or deactivated by triggers based on user inputs. The user chooses among button choices and inputs numeric values on the first screen, and the inputs determine the activation of the triggers. (Figure 1-Left Panel) We used Google App Script to build a spreadsheet-based tool (‘editing app’) where the editing experts (‘authors’) can define data entry elements (e.g., segmented buttons, pickers, numeric or text data field, multiple-item selectors, or multimedia inputs) for the data inputs. The editing authors are allowed to set triggers to each data entry item, allowing branching of questions and the end-users being exposed only to a subset of questions at each usage. The expert authors can name and assign variables to specific questions (e.g., the answer to the question of the presence of symptoms can be saved to a variable named ‘symptoms’). The authors can also assign boolean triggers to the text contents as we discussed above. When the authors finish editing the contents and decide to update a clinical module, the app script packages the contents into multiple CSV files (dashboard, pages, contents of each page, and references) and sends the fi les to Google Firebase, which is Google’s real-time database and backend for mobile apps. We developed the front-end side of the app service in XCODE, which is an app development environment for the Apple iOS platform. The entire code is written in Swift.

, pages, contents of each page, and references) and sends the fi les to Google Firebase, which is Google’s real-time database and backend for mobile apps. We developed the front-end side of the app service in XCODE, which is an app development environment for the Apple iOS platform. The entire code is written in Swift. Seven clinicians without programming skills tested the editing app to build drafts of test apps for several clinical problems and to improve participatory aspects of the framework. A senior endocrinologist reviewed and edited all of the contents of the app for both accuracy and ease of understanding.

, pages, contents of each page, and references) and sends the fi les to Google Firebase, which is Google’s real-time database and backend for mobile apps. We developed the front-end side of the app service in XCODE, which is an app development environment for the Apple iOS platform. The entire code is written in Swift. Seven clinicians without programming skills tested the editing app to build drafts of test apps for several clinical problems and to improve participatory aspects of the framework. A senior endocrinologist reviewed and edited all of the contents of the app for both accuracy and ease of understanding. Development of the DKA/HHS module The ADA consensus guidelines, including the DKA/HHS flowchart, were used as the basis of our decision framework. [13,14] The first step for the user in our framework is to choose DKA or HHS. An info button is provided for clinicians who wish for more information in making this decision. DKA is chosen as a default in case the user is not sure and wishes to proceed to the next question without choosing. Next, the user is asked if fluid resuscitation was started. If it was not, the application prompts the user to begin fluid boluses. The next input is regarding suspected hypovolemia. Here too, if the user chooses shock, the application will recommend to treat shock before proceeding. If the user chooses severe hypovolemia, the application will allow for the rest of the inputs, but will recommend to continue giving 0.9% NaCl boluses (1L/hr). (Figure 1-Middle Panel). The next input fields are for weight, glucose, and electrolytes. The application calculates the anion gap and corrected sodium, and guides the clinician as to the appropriate fluids and rate, the need for potassium repletion, the type and rate of insulin administration (and whether it should even be started), and the need for continued monitoring (Figure 1-Middle and Right Panel).

. The application calculates the anion gap and corrected sodium, and guides the clinician as to the appropriate fluids and rate, the need for potassium repletion, the type and rate of insulin administration (and whether it should even be started), and the need for continued monitoring (Figure 1-Middle and Right Panel). Emergent inputs, such as choosing “Shock” for the question “Suspected Hypovolemia?” will trigger an emergent response: “Give fluids, start hemodynamic monitoring, and consider pressors if clinically indicated.” A text paragraph is provided to further educate the clinician with additional details. In this case, it would read, “Shock requires rapid treatment with fluid resuscitation (rapid boluses of 0.9% NaCl), hemodynamic monitoring, and pressors if indicated. Stabilize patient first.” References are provided for each of these text paragraphs.

s provided to further educate the clinician with additional details. In this case, it would read, “Shock requires rapid treatment with fluid resuscitation (rapid boluses of 0.9% NaCl), hemodynamic monitoring, and pressors if indicated. Stabilize patient first.” References are provided for each of these text paragraphs. Discussion and future directions To our knowledge, there are no currently existing apps that guide clinicians through the process of DKA/HHS management while providing education. Existing systems attempt to use either static informational pages (e.g., guidelines or UpToDate), calculators (e.g., MDCalc), or automatic order sets to help improve access to and implementation of guidelines [22]. Our CDS tool is unique in that it delivers tailored aspects of the management with additional information specific to individual clinical scenarios at the point of care. In doing so, it not only provides clinical decision support, but is a resource for continual education for the clinician. Our app is free to download on the Apple App Store. Since it currently does not link or store any personal patient data, there are no HIPPA concerns. While our app can potentially improve care and decrease mortality, further testing is required for confirmation.

is a resource for continual education for the clinician. Our app is free to download on the Apple App Store. Since it currently does not link or store any personal patient data, there are no HIPPA concerns. While our app can potentially improve care and decrease mortality, further testing is required for confirmation. We plan to conduct formal real-world validation by means of measuring the systemic usability scale (SUS), physician satisfaction and decision understanding at the beginning stage of our implementation and using participatory design to build future versions of our app based on input. We will also trial the implementation of our app in a hospital setting and use an interrupted time-series design to assess its effects with each clinician as a target of randomization. Since our app provides easier access to guidelines, rather than a promotion of unique guidelines, the ethical concerns are minimal, similar to existing guideline repositories. If successful, we will spread awareness through physician communities by publishing our usability testing and evaluation results. We will also ask users to rate our app based on ease of use, usefulness of the tool, usefulness of the content, and overall satisfaction. This optional survey will ask for comments, as well, helping us to improve the tool. Finally, our tool may be applied to a vast array of diseases such as thyroid storm, hypothyroidism and others.

We plan to conduct formal real-world validation by means of measuring the systemic usability scale (SUS), physician satisfaction and decision understanding at the beginning stage of our implementation and using participatory design to build future versions of our app based on input. We will also trial the implementation of our app in a hospital setting and use an interrupted time-series design to assess its effects with each clinician as a target of randomization. Since our app provides easier access to guidelines, rather than a promotion of unique guidelines, the ethical concerns are minimal, similar to existing guideline repositories. If successful, we will spread awareness through physician communities by publishing our usability testing and evaluation results. We will also ask users to rate our app based on ease of use, usefulness of the tool, usefulness of the content, and overall satisfaction. This optional survey will ask for comments, as well, helping us to improve the tool. Finally, our tool may be applied to a vast array of diseases such as thyroid storm, hypothyroidism and others. Conclusion Elevated mortality in hyperglycemic crisis among various racial groups and across geographic areas is unacceptable and requires novel interventions. We built a clinical decision support tool that systematizes and personalizes the treatment of DKA and HHS by bringing point-of-care access to the guidelines specific to individual cases to the clinician’s hands. This is potentially the solution to close the mortality disparity gap for DKA and HHS.

novel interventions. We built a clinical decision support tool that systematizes and personalizes the treatment of DKA and HHS by bringing point-of-care access to the guidelines specific to individual cases to the clinician’s hands. This is potentially the solution to close the mortality disparity gap for DKA and HHS. Acknowledgement This work is supported, in part, by the efforts of Dr. Moro O. Salifu M.D., M.P.H., M.B.A., M.A.C.P., Professor and Chairman of Medicine through NIH Grant number S21MD012474. Figure 1: Example of input (Left Panel) and personalizex content (Middle and Right Panel).