Sujay Kakarmath

Diabetes in sub-Saharan Africa: from clinical care to health policy

Harvard TH Chan School of Public Health, Harvard University, Boston, MA, USA (Prof R Atun FRCP, Prof T Bärnighausen MD, I Postolovska ScD, S Vollmer PhD, B Ammon, A Annamreddi, A Awasthi, S Bhadriraju, J Chai MPH, J Ho BS, S S Kakarmath MBBS MS, R Kharel, M A Kyle, S C Lee MD, A Lichtman MD, J Manne-Goehler MD, M Nair MPH, O L O Okafor MPH, O Okunade MD, D Sando, A Sharma MPH, A S Syed MPH); Harvard Medical School, Harvard University, Boston, MA, USA (Prof R Atun, A Binagwaho MD, P Chipendo MD, J Manne-Goehler); Centre for Global Health, King’s College London, Weston Education Centre, London, UK (J I Davies MD); MRC/Wits Rural Public Health and Health Transitions Research Unit, School of Public Health, Education Campus, University of Witwatersrand, Parktown, South Africa (J I Davies); University of Bristol, Bristol, UK (E A M Gale FRCP); Muhimbili University of Health and Allied Sciences, and Abbas Medical Centre, Dar es Salaam, Tanzania (Z G Abbas MMed); Institute of Public Health, Faculty of Diabetes in sub-Saharan Africa: from clinical care to health policy

Stroke Is the Leading Cause of Death in Rural Gadchiroli, India A Prospective Community-Based Study

Background and Purpose— Stroke is an important cause of death and disability worldwide. However, information on stroke deaths in rural India is scarce. To measure the mortality burden of stroke, we conducted a community-based study in a rural area of Gadchiroli, one of the most backward districts of India. Methods— We prospectively collected information on all deaths from April 2011 to March 2013 and assigned causes of death using a well-validated verbal autopsy tool in a rural population of 94 154 individuals residing in 86 villages. Two trained physicians independently assigned the cause of death, and the disagreements were resolved by a third physician. Results— Of 1599 deaths during the study period, 229 (14.3%) deaths were caused by stroke. Stroke was the most frequent cause of death. For those who died because of stroke, the mean age was 67.47±11.8 years and 48.47% were women. Crude stroke mortality rate was 121.6 (95% confidence interval, 106.4–138.4), and age-standardized stroke mortality rate was 191.9 (95% confidence interval, 165.8–221.1) per 100 000 population. Of total stroke deaths, 87.3% stroke deaths occurred at home and 46.3% occurred within the first month from the onset of symptoms. Conclusions— Stroke is the leading cause of death and accounted for 1 in 7 deaths in this rural community in Gadchiroli. There was high early mortality, and the mortality rate because of stroke was higher than that reported from previous studies from India. Stroke is emerging as a public health priority in rural India.

Validating a Machine Learning Algorithm to Predict 30-Day Re-Admissions in Patients With Heart Failure: Protocol for a Prospective Cohort Study

Background Big data solutions, particularly machine learning predictive algorithms, have demonstrated the ability to unlock value from data in real time in many settings outside of health care. Rapid growth in electronic medical record adoption and the shift from a volume-based to a value-based reimbursement structure in the US health care system has spurred investments in machine learning solutions. Machine learning methods can be used to build flexible, customized, and automated predictive models to optimize resource allocation and improve the efficiency and quality of health care. However, these models are prone to the problems of overfitting, confounding, and decay in predictive performance over time. It is, therefore, necessary to evaluate machine learning–based predictive models in an independent dataset before they can be adopted in the clinical practice. In this paper, we describe the protocol for independent, prospective validation of a machine learning–based model trained to predict the risk of 30-day re-admission in patients with heart failure. Objective This study aims to prospectively validate a machine learning–based predictive model for inpatient admissions in patients with heart failure by comparing its predictions of risk for 30-day re-admissions against outcomes observed prospectively in an independent patient cohort. Methods All adult patients with heart failure who are discharged alive from an inpatient admission will be prospectively monitored for 30-day re-admissions through reports generated by the electronic medical record system. Of these, patients who are part of the training dataset will be excluded to avoid information leakage to the algorithm. An expected sample size of 1228 index admissions will be required to observe a minimum of 100 30-day re-admission events. Deidentified structured and unstructured data will be fed to the algorithm, and its prediction will be recorded. The overall model performance will be assessed using the concordance statistic. Furthermore, multiple discrimination thresholds for screening high-risk patients will be evaluated according to the sensitivity, specificity, predictive values, and estimated cost savings to our health care system. Results The project received funding in April 2017 and data collection began in June 2017. Enrollment was completed in July 2017. Data analysis is currently underway, and the first results are expected to be submitted for publication in October 2018. Conclusions To the best of our knowledge, this is one of the first studies to prospectively evaluate a predictive machine learning algorithm in a real-world setting. Findings from this study will help to measure the robustness of predictions made by machine learning algorithms and set a realistic benchmark for expectations of gains that can be made through its application to health care. Registered Report Identifier RR1-10.2196/9466

Feasibility of Using an Automated Device (iThermonitor) for Continuous Temperature Monitoring in Pediatric Patients (Preprint)

Background Fever is an important vital sign and often the first one to be assessed in a sick child. In acutely ill children, caregivers are expected to monitor a child’s body temperature at home after an initial medical consult. Fever literacy of many caregivers is known to be poor, leading to fever phobia. In children with a serious illness, the responsibility of periodically monitoring temperature can add substantially to the already stressful experience of caring for a sick child. Objective The objective of this pilot study was to assess the feasibility of using the iThermonitor, an automated temperature measurement device, for continuous temperature monitoring in postoperative and postchemotherapy pediatric patients. Methods We recruited 25 patient-caregiver dyads from the Pediatric Surgery Department at the Massachusetts General Hospital (MGH) and the Pediatric Cancer Centers at the MGH and the Dana Farber Cancer Institute. Enrolled dyads were asked to use the iThermonitor device for continuous temperature monitoring over a 2-week period. Surveys were administered to caregivers at enrollment and at study closeout. Caregivers were also asked to complete a daily event-monitoring log. The Generalized Anxiety Disorder-7 item questionnaire was also used to assess caregiver anxiety at enrollment and closeout. Results Overall, 19 participant dyads completed the study. All 19 caregivers reported to have viewed temperature data on the study-provided iPad tablet at least once per day, and more than a third caregivers did so six or more times per day. Of all participants, 74% (14/19) reported experiencing an out-of-range temperature alert at least once during the study. Majority of caregivers reported that it was easy to learn how to use the device and that they felt confident about monitoring their child’s temperature with it. Only 21% (4/9) of caregivers reported concurrently using a device other than the iThermonitor to monitor their child’s temperature during the study. Continuous temperature monitoring was not associated with an increase in caregiver anxiety. Conclusions The study results reveal that the iThermonitor is a highly feasible and easy-to-use device for continuous temperature monitoring in pediatric oncology and surgery patients. Trial Registration ClinicalTrials.gov NCT02410252; https://clinicaltrials.gov/ct2/show/NCT02410252 (Archived by WebCite at http://www.webcitation.org/73LnO7hel)

Diabetes in sub-Saharan Africa

Harvard TH Chan School of Public Health, Harvard University, Boston, MA, USA (Prof R Atun FRCP, Prof T Bärnighausen MD, I Postolovska ScD, S Vollmer PhD, B Ammon, A Annamreddi, A Awasthi, S Bhadriraju, J Chai MPH, J Ho BS, S S Kakarmath MBBS MS, R Kharel, M A Kyle, S C Lee MD, A Lichtman MD, J Manne-Goehler MD, M Nair MPH, O L O Okafor MPH, O Okunade MD, D Sando, A Sharma MPH, A S Syed MPH); Harvard Medical School, Harvard University, Boston, MA, USA (Prof R Atun, A Binagwaho MD, P Chipendo MD, J Manne-Goehler); Centre for Global Health, King’s College London, Weston Education Centre, London, UK (J I Davies MD); MRC/Wits Rural Public Health and Health Transitions Research Unit, School of Public Health, Education Campus, University of Witwatersrand, Parktown, South Africa (J I Davies); University of Bristol, Bristol, UK (E A M Gale FRCP); Muhimbili University of Health and Allied Sciences, and Abbas Medical Centre, Dar es Salaam, Tanzania (Z G Abbas MMed); Institute of Public Health, Faculty of Diabetes in sub-Saharan Africa: from clinical care to health policy

Dietary determinants of serum total cholesterol among middle-aged and older adults: a population-based cross-sectional study in Dar es Salaam, Tanzania

Objective To assess the dietary determinants of serum total cholesterol. Design Cross-sectional population-based study. Setting Peri-urban region of Dar es Salaam, Tanzania. Participants 347 adults aged 40 years and older from the Dar es Salaam Urban Cohort Hypertension Study. Main outcome measure Serum total cholesterol measured using a point-of-care device. Results Mean serum total cholesterol level was 204 mg/dL (IQR 169–236 mg/dL) in women and 185 mg/dL (IQR 152–216 mg/dL) in men. After adjusting for demographic, socioeconomic, lifestyle and dietary factors, participants who reported using palm oil as the major cooking oil had serum total cholesterol higher by 15 mg/dL (95% CI 1 to 29 mg/dL) compared with those who reported using sunflower oil. Consumption of one or more servings of meat per day (p for trend=0.017) and less than five servings of fruits and vegetables per day (p for trend=0.024) were also associated with higher serum total cholesterol. A combination of using palm oil for cooking, eating more than one serving of meat per day and fewer than five servings of fruits and vegetables per day, was associated with 46 mg/dL (95% CI 16 to 76 mg/dL) higher serum total cholesterol. Conclusions Using palm oil for cooking was associated with higher serum total cholesterol levels in this peri-urban population in Dar es Salaam. Reduction of saturated fat content of edible oil may be considered as a population-based strategy for primary prevention of cardiovascular diseases.