Revealing the Mysteries of Population Mobility Amid the COVID-19 Pandemic in Canada: Comparative Analysis With Internet of Things-Based Thermostat Data and Google Mobility Insights.

BACKGROUND: The COVID-19 pandemic necessitated public health policies to limit human mobility and curb infection spread. Human mobility, which is often underestimated, plays a pivotal role in health outcomes, impacting both infectious and chronic diseases. Collecting precise mobility data is vital for understanding human behavior and informing public health strategies. Google's GPS-based location tracking, which is compiled in Google Mobility Reports, became the gold standard for monitoring outdoor mobility during the pandemic. However, indoor mobility remains underexplored. OBJECTIVE: This study investigates in-home mobility data from ecobee's smart thermostats in Canada (February 2020 to February 2021) and compares it directly with Google's residential mobility data. By assessing the suitability of smart thermostat data, we aim to shed light on indoor mobility patterns, contributing valuable insights to public health research and strategies. METHODS: Motion sensor data were acquired from the ecobee "Donate Your Data" initiative via Google's BigQuery cloud platform. Concurrently, residential mobility data were sourced from the Google Mobility Report. This study centered on 4 Canadian provinces-Ontario, Quebec, Alberta, and British Columbia-during the period from February 15, 2020, to February 14, 2021. Data processing, analysis, and visualization were conducted on the Microsoft Azure platform using Python (Python Software Foundation) and R programming languages (R Foundation for Statistical Computing). Our investigation involved assessing changes in mobility relative to the baseline in both data sets, with the strength of this relationship assessed using Pearson and Spearman correlation coefficients. We scrutinized daily, weekly, and monthly variations in mobility patterns across the data sets and performed anomaly detection for further insights. RESULTS: The results revealed noteworthy week-to-week and month-to-month shifts in population mobility within the chosen provinces, aligning with pandemic-driven policy adjustments. Notably, the ecobee data exhibited a robust correlation with Google's data set. Examination of Google's daily patterns detected more pronounced mobility fluctuations during weekdays, a trend not mirrored in the ecobee data. Anomaly detection successfully identified substantial mobility deviations coinciding with policy modifications and cultural events. CONCLUSIONS: This study's findings illustrate the substantial influence of the Canadian stay-at-home and work-from-home policies on population mobility. This impact was discernible through both Google's out-of-house residential mobility data and ecobee's in-house smart thermostat data. As such, we deduce that smart thermostats represent a valid tool for facilitating intelligent monitoring of population mobility in response to policy-driven shifts.

Community-friendly tool to assess patient satisfaction in tuberculosis programme in the covid pandemic period; Bhubaneswar; India.

National Tuberculosis Elimination Programme (NTEP) is a priority programme for India, given that India is one of the 20 countries with high burden of TB. Odisha (a state in Eastern India) in 2017 reported 159/lakh/year cases as against a national average incidence of 138.33/lakh/year. Thus, the state, under an encouraging political milieu went to vigorously implement the newer initiatives outlined in the National Strategic Plan 2020-25, the result of which in 2021 Odisha was ranked second in the country for its efforts on TB elimination. The current article attempts to take community feedback on the programmatic endeavours, by using a tool for client satisfaction. 350 consecutive subjects, adults aged 18 years and above consented among the 465 who were diagnosed and started on treatment between 5/4/21 to 5/4/22. The selected subjects were interviewed after confirmation of diagnosis at one DOTS centre in an urban city, using a pre-designed and pretested tool after taking requisite ethical permission from the institute as well as after consent from the participating subjects. The tool had 10 items on structure; 10 items on the process and 3 on outcome each rated on a Likert scale of 1-5 (very satisfied to very dissatisfied) and lastly a score on 10 scale for overall satisfaction. For all the 24 items; alpha Cronbach coefficient was 0.928 (bootstrap 95% CI); for subscales infrastructure, process and outcome isolatedly was 0.931, 0.912 and 0.959 respectively. This shows that the questionnaire had very good reliability. Infrastructure mean score for all 10 questions was above 4.5; for processes, it was <4.05 for a few questions and mainly these referred to Out of pocket expenditures and waiting time; outcome again for all three questions mean score was near or above 4.4. The overall score was between 5 and 10; maximally at 8. This simple tool gave clear-cut hints at the best picture scenario, as the study was done at a single DOTS service centre in the capital city of the state, which ran effectively even during the pandemic. However, it brings out the weak points in the processes like the cost incurred to come to the centre and communication with ancillary staff. No difference in satisfaction levels was reported among pulmonary and extrapulmonary cases (ratio 8.4:1.5) in this study in the covid period; with overall satisfaction being 4.45 ± 0.44 and 4.41 ± 0.25 respectively. The promptness in the programmatic services at the DOTS centre under study is encouraging but warrants conformity with DOTS centres in rural and far-to-reach areas. Best evaluation of achievements of programme can be determined by word of mouth of the beneficiaries. Hence, this tool if replicated at all service centres can help programme managers plug any disconnects in service delivery and assure good satisfaction from all quarters.

Health Monitoring Using Smart Home Technologies: Scoping Review.

BACKGROUND: The Internet of Things (IoT) has become integrated into everyday life, with devices becoming permanent fixtures in many homes. As countries face increasing pressure on their health care systems, smart home technologies have the potential to support population health through continuous behavioral monitoring. OBJECTIVE: This scoping review aims to provide insight into this evolving field of research by surveying the current technologies and applications for in-home health monitoring. METHODS: Peer-reviewed papers from 2008 to 2021 related to smart home technologies for health care were extracted from 4 databases (PubMed, Scopus, ScienceDirect, and CINAHL); 49 papers met the inclusion criteria and were analyzed. RESULTS: Most of the studies were from Europe and North America. The largest proportion of the studies were proof of concept or pilot studies. Approximately 78% (38/49) of the studies used real human participants, most of whom were older females. Demographic data were often missing. Nearly 60% (29/49) of the studies reported on the health status of the participants. Results were primarily reported in engineering and technology journals. Almost 62% (30/49) of the studies used passive infrared sensors to report on motion detection where data were primarily binary. There were numerous data analysis, management, and machine learning techniques employed. The primary challenges reported by authors were differentiating between multiple participants in a single space, technology interoperability, and data security and privacy. CONCLUSIONS: This scoping review synthesizes the current state of research on smart home technologies for health care. We were able to identify multiple trends and knowledge gaps-in particular, the lack of collaboration across disciplines. Technological development dominates over the human-centric part of the equation. During the preparation of this scoping review, we noted that the health care research papers lacked a concrete definition of a smart home, and based on the available evidence and the identified gaps, we propose a new definition for a smart home for health care. Smart home technology is growing rapidly, and interdisciplinary approaches will be needed to ensure integration into the health sector.

Prevalence of smokeless tobacco use in India and its association with various occupations: A LASI study.

Background: More than two-thirds of deaths in developing countries are due to non-communicable diseases, and tobacco is a leading risk factor. There are numerous different socio-demographic factors that impact on the use of smokeless tobacco, of which occupation is one. The objectives of this study are to find out the overall prevalence of smokeless tobacco use (ever and current use), the pattern of association with various occupations and related variables (current and past workers), and the role of childhood adversity on initiation and use. Methods: This study used data from the Longitudinal Aging Study in India (LASI) wave 1, a nationally representative cross-sectional study collected in 2017-18. Current and previous users of smokeless tobacco are taken into consideration as the target population. For the data analysis, survey-weighted tools have been applied for descriptive statistics and multivariable logistic regression model. The weighted data analysis has been done using R studio with R version 4. Results and discussion: From the sample size of 65,561, 38% have used either smoking or smokeless tobacco. Among them, 40% use tobacco in smoke form, 51% use smokeless tobacco, and 9% take both. At the population level, 22.8 and 20.4% are previous and current users of smokeless tobacco, respectively. Type of occupation, type of employer, place of work, kind of business, and workload were found to be significantly associated with smokeless tobacco use. A deaddiction and tobacco quitting policy targeting rural male informal workers should be the focus of the Government.

Effect of COVID-19 pandemic on home delivery of contraceptives by community health workers in India: Time to (re) evaluate and innovate.

The World Health Organization (WHO) declared COVID-19 a global health emergency in January 2020, leading to a nationwide lockdown in India. It has been an experience from other outbreaks that governments cannot maintain the essential health services and guarantee health services. Due to COVID-19-related case management, all health schemes, including FP services, have been disrupted globally regarding availability, accessibility, appropriateness of service delivery, adequacy, and continuity of care. The impact of the pandemic on FP services listed includes disruptions in supply chain management, enhanced gender inequity, communication barriers, fear of going outside and buying contraceptives, discontinuity of ASHA capacity building, increased time spent with all family members, reverse migration of workers, and increased need of contraceptive commodities. Evidence shows the consequence of non-supply of logistics, social distancing, inadequate human resources, and inability to access services might result in 26 million couples in unmet need for contraception, resulting in 2.4 million unintended pregnancies and 1.45 million abortions, which may lead to unsafe abortions. Potential solutions to these problems include telephonic service delivery, maintaining a record, using video communication and other technological solutions using a smartphone, combining routine immunization with FP services, and installing self-dispensing machines for contraceptives at accessible places. The limitation of this work is that this is wholly experienced-based work and not based on primary findings from the field level data. These findings highlight the importance of reproductive health needs during the pandemic and guide policymakers.

Factors Influencing the Adoption of Contact Tracing Applications: Systematic Review and Recommendations.

Background: The emergence of new variants of COVID-19 causing breakthrough infections and the endemic potential of the coronavirus are an indication that digital contact tracing apps (CTAs) may continue to be useful for the long haul. However, the uptake of these apps in many countries around the world has been low due to several factors militating against their adoption and usage. Objective: In this systematic review, we set out to uncover the key factors that facilitate or militate against the adoption of CTAs, which researchers, designers and other stakeholders should focus on in future iterations to increase their adoption and effectiveness in curbing the spread of COVID-19. Data Sources: Seven databases, including PubMed, CINAHL, Scopus, Web of Service, IEEE Xplore, ACM Digital Library, and Google Scholar, were searched between October 30 and January 31, 2020. A total of 777 articles were retrieved from the databases, with 13 of them included in the systematic review after screening. Study Eligibility Criteria Participants and Intervention: The criteria for including articles in the systematic review were that they could be user studies from any country around the world, must be peer-reviewed, written in English, and focused on the perception and adoption of COVID-19 contact tracing and/or exposure notification apps. Other criteria included user study design could be quantitative, qualitative, or mixed, and must have been conducted during the COVID-19 pandemic, which began in the early part of 2020. Study Appraisal and Synthesis Methods: Three researchers searched seven databases (three by the first author, and two each by the second and third authors) and stored the retrieved articles in a collaborative Mendeley reference management system online. After the removal of duplicates, each researcher independently screened one third of the articles based on title/abstract. Thereafter, all three researchers collectively screened articles that were in the borderline prior to undergoing a full-text review. Then, each of the three researchers conducted a full-text review of one-third of the eligible articles to decide the final articles to be included in the systematic review. Next, all three researchers went through the full text of each borderline article to determine their appropriateness and relevance. Finally, each researcher extracted the required data from one-third of the included articles into a collaborative Google spreadsheet and the first author utilized the data to write the review. Results: This review identified 13 relevant articles, which found 56 factors that may positively or negatively impact the adoption of CTAs. The identified factors were thematically grouped into ten categories: privacy and trust, app utility, facilitating conditions, social-cognitive factors, ethical concerns, perceived technology threats, perceived health threats, technology familiarity, persuasive design, and socio-demographic factors. Of the 56 factors, privacy concern turned out to be the most frequent factor of CTA adoption (12/13), followed by perceived benefit (7/13), perceived trust (6/13), and perceived data security risk (6/13). In the structural equation models presented by the authors of the included articles, a subset of the 56 elicited factors (e.g., perceived benefit and privacy concern) explains 16 to 77% of the variance of users' intention to download, install, or use CTAs to curb the spread of COVID-19. Potential adoption rates of CTA range from 19% (in Australia) to 75% (in France, Italy, Germany, United Kingdom, and United States). Moreover, actual adoption rates range from 37% (in Australia) to 50% (in Germany). Finally, most of the studies were carried out in Europe (66.7%), followed by North America (13.3%), and Australia, Asia, and South America (6.7% each). Conclusion: The results suggest that future CTA iterations should give priority to privacy protection through minimal data collection and transparency, improving contact tracing benefits (personal and social), and fostering trust through laudable gestures such as delegating contact tracing to public health authorities, making source code publicly available and stating who will access user data, when, how, and what it will be used for. Moreover, the results suggest that data security and tailored persuasive design, involving reward, self-monitoring, and social-location monitoring features, have the potential of improving CTA adoption. Hence, in addition to addressing issues relating to utility, privacy, trust, and data security, we recommend the integration of persuasive features into future designs of CTAs to improve their motivational appeal, adoption, and the user experience. Systematic Review Registration: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021259080 PROSPERO, identifier CRD42021259080.

Usability of Smart Home Thermostat to Evaluate the Impact of Weekdays and Seasons on Sleep Patterns and Indoor Stay: Observational Study.

BACKGROUND: Sleep behavior and time spent at home are important determinants of human health. Research on sleep patterns has traditionally relied on self-reported data. Not only does this methodology suffer from bias but the population-level data collection is also time-consuming. Advances in smart home technology and the Internet of Things have the potential to overcome these challenges in behavioral monitoring. OBJECTIVE: The objective of this study is to demonstrate the use of smart home thermostat data to evaluate household sleep patterns and the time spent at home and how these behaviors are influenced by different weekdays and seasonal variations. METHODS: From the 2018 ecobee Donate your Data data set, 481 North American households were selected based on having at least 300 days of data available, equipped with ≥6 sensors, and having a maximum of 4 occupants. Daily sleep cycles were identified based on sensor activation and used to quantify sleep time, wake-up time, sleep duration, and time spent at home. Each household's record was divided into different subsets based on seasonal, weekday, and seasonal weekday scales. RESULTS: Our results demonstrate that sleep parameters (sleep time, wake-up time, and sleep duration) were significantly influenced by the weekdays. The sleep time on Fridays and Saturdays is greater than that on Mondays, Wednesdays, and Thursdays (n=450; P<.001; odds ratio [OR] 1.8, 95% CI 1.5-3). There is significant sleep duration difference between Fridays and Saturdays and the rest of the week (n=450; P<.001; OR 1.8, 95% CI 1.4-2). Consequently, the wake-up time is significantly changing between weekends and weekdays (n=450; P<.001; OR 5.6, 95% CI 4.3-6.3). The results also indicate that households spent more time at home on Sundays than on the other weekdays (n=445; P<.001; OR 2.06, 95% CI 1.64-2.5). Although no significant association is found between sleep parameters and seasonal variation, the time spent at home in the winter is significantly greater than that in summer (n=455; P<.001; OR 1.6, 95% CI 1.3-2.3). These results are in accordance with existing literature. CONCLUSIONS: This is the first study to use smart home thermostat data to monitor sleep parameters and time spent at home and their dependence on weekday, seasonal, and seasonal weekday variations at the population level. These results provide evidence of the potential of using Internet of Things data to help public health officials understand variations in sleep indicators caused by global events (eg, pandemics and climate change).

Cost-Effectiveness of Population Screening Programs for Cardiovascular Diseases and Diabetes in Low- and Middle-Income Countries: A Systematic Review.

Almost all low- and middle-income countries (LMICs) have instated a program to control and manage non-communicable diseases (NCDs). Population screening is an integral component of this strategy and requires a substantial chunk of investment. Therefore, testing the screening program for economic along with clinical effectiveness is essential. There is significant proof of the benefits of incorporating economic evidence in health decision-making globally, although evidence from LMICs in NCD prevention is scanty. This systematic review aims to consolidate and synthesize economic evidence of screening programs for cardiovascular diseases (CVD) and diabetes from LMICs. The study protocol is registered on PROSPERO (CRD42021275806). The review includes articles from English and Chinese languages. An initial search retrieved a total of 2,644 potentially relevant publications. Finally, 15 articles (13 English and 2 Chinese reports) were included and scrutinized in detail. We found 6 economic evaluations of interventions targeting cardiovascular diseases, 5 evaluations of diabetes interventions, and 4 were combined interventions, i.e., screening of diabetes and cardiovascular diseases. The study showcases numerous innovative screening programs that have been piloted, such as using mobile technology for screening, integrating non-communicable disease screening with existing communicable disease screening programs, and using community health workers for screening. Our review reveals that context is of utmost importance while considering any intervention, i.e., depending on the available resources, cost-effectiveness may vary-screening programs can be made universal or targeted just for the high-risk population.

NextGen Public Health Surveillance and the Internet of Things (IoT).

Recent advances in technology have led to the rise of new-age data sources (e.g., Internet of Things (IoT), wearables, social media, and mobile health). IoT is becoming ubiquitous, and data generation is accelerating globally. Other health research domains have used IoT as a data source, but its potential has not been thoroughly explored and utilized systematically in public health surveillance. This article summarizes the existing literature on the use of IoT as a data source for surveillance. It presents the shortcomings of current data sources and how NextGen data sources, including the large-scale applications of IoT, can meet the needs of surveillance. The opportunities and challenges of using these modern data sources in public health surveillance are also explored. These IoT data ecosystems are being generated with minimal effort by the device users and benefit from high granularity, objectivity, and validity. Advances in computing are now bringing IoT-based surveillance into the realm of possibility. The potential advantages of IoT data include high-frequency, high volume, zero effort data collection methods, with a potential to have syndromic surveillance. In contrast, the critical challenges to mainstream this data source within surveillance systems are the huge volume and variety of data, fusing data from multiple devices to produce a unified result, and the lack of multidisciplinary professionals to understand the domain and analyze the domain data accordingly.

Effect of Community Education Program on Stroke Symptoms and Treatment on School and College Students from South India: A Longitudinal Observational Study.

Community awareness regarding stroke signs, risk factors, and actions that help reduce the risk and complications of stroke is poorly addressed, as it is thought to be the best approach to control and prevent stroke. Aim: To establish the awareness of stroke and its management among high school and college students using an educational intervention. A questionnaire was administered to students from five high schools and four colleges with different areas of focus, (arts, science and commerce), types (public, semi-public and private), and economic locations before and after an educational lecture on stroke. The lecture covered the following elements: stroke definition, signs, risk factors, actions, time window for thrombolytic therapy, and types of rehabilitation interventions. This study included 1036 participants, of whom 36.3% were male and 56.4% were high school students, and the mean age was 17.15 ± 1.29 (15-22) years. Before the lecture, 147 participants were unaware of a single sign of stroke, and 124 did not know the risk factors. After the intervention, 439 participants knew four signs of stroke, and 196 knew 12 risk factors. Female students had better knowledge about stroke signs (odds ratio (OR), 3.08; 95% confidence interval (95% CI), 2.15-4.43). Hypertension (52.7%) and weakness (59.85%) were the most known signs and risk factors. The proportion of students who selected traditional medicine as the mode of treatment decreased from 34.75% to 8.59% after the lecture. Other rehabilitation methods (e.g., physical therapy, occupational therapy, speech therapy and counseling) were chosen by more than 80% of the students. The results of the current study showed that the awareness on stroke risk factors and management among the school and college students can be significantly improved with regular educational interventions, and therefore stroke can be prevented to some extent.

Factors Influencing the Adoption of Contact Tracing Applications: Protocol for a Systematic Review.

BACKGROUND: Following the onset of the COVID-19 pandemic, digital contact tracing apps have become prevalent worldwide in a coordinated effort to curb the spread of COVID-19. However, their uptake has been low and slow due to privacy concerns, the lack of trust and motivational affordances, and their minimalist design. OBJECTIVE: The objective of this article is to present a protocol for a systematic review of the main factors, including facilitators and barriers, that influence the adoption of contact tracing apps. METHODS: We searched seven databases, namely, Scopus, CINAHL, PubMed (MEDLINE), IEEE Xplore Digital Library, Association for Computing Machinery (ACM) Digital Library, Web of Science, and Google Scholar, for relevant publications between October 30, 2020, and January 31, 2021. Three authors were involved in removing duplicates, screening, and selection of relevant articles according to the PRISMA-P (Preferred Reporting Items for Systematic Review and Meta-analysis Protocols) guidelines. RESULTS: Altogether, we retrieved 777 articles from the seven databases. As of May 14, 2021, we have completed the screening process and arrived at 13 eligible articles to be included in the systematic review. We hope to elicit, summarize, and report the main findings in the systematic review article by the end of August 2021. We expect to uncover facilitators and barriers related to app utility, data security, ease of use, and persuasive design that are deemed important to adoption of contact tracing apps. CONCLUSIONS: The findings of the systematic review will help researchers to uncover the gaps in the adoption of contact tracing apps, and decision makers and designers to focus on the principal adoption factors necessary to create better and more effective contact tracing apps. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/28961.

Understanding User Behavior Through the Use of Unsupervised Anomaly Detection: Proof of Concept Using Internet of Things Smart Home Thermostat Data for Improving Public Health Surveillance.

BACKGROUND: One of the main concerns of public health surveillance is to preserve the physical and mental health of older adults while supporting their independence and privacy. On the other hand, to better assist those individuals with essential health care services in the event of an emergency, their regular activities should be monitored. Internet of Things (IoT) sensors may be employed to track the sequence of activities of individuals via ambient sensors, providing real-time insights on daily activity patterns and easy access to the data through the connected ecosystem. Previous surveys to identify the regular activity patterns of older adults were deficient in the limited number of participants, short period of activity tracking, and high reliance on predefined normal activity. OBJECTIVE: The objective of this study was to overcome the aforementioned challenges by performing a pilot study to evaluate the utilization of large-scale data from smart home thermostats that collect the motion status of individuals for every 5-minute interval over a long period of time. METHODS: From a large-scale dataset, we selected a group of 30 households who met the inclusion criteria (having at least 8 sensors, being connected to the system for at least 355 days in 2018, and having up to 4 occupants). The indoor activity patterns were captured through motion sensors. We used the unsupervised, time-based, deep neural-network architecture long short-term memory-variational autoencoder to identify the regular activity pattern for each household on 2 time scales: annual and weekday. The results were validated using 2019 records. The area under the curve as well as loss in 2018 were compatible with the 2019 schedule. Daily abnormal behaviors were identified based on deviation from the regular activity model. RESULTS: The utilization of this approach not only enabled us to identify the regular activity pattern for each household but also provided other insights by assessing sleep behavior using the sleep time and wake-up time. We could also compare the average time individuals spent at home for the different days of the week. From our study sample, there was a significant difference in the time individuals spent indoors during the weekend versus on weekdays. CONCLUSIONS: This approach could enhance individual health monitoring as well as public health surveillance. It provides a potentially nonobtrusive tool to assist public health officials and governments in policy development and emergency personnel in the event of an emergency by measuring indoor behavior while preserving privacy and using existing commercially available thermostat equipment.

Enabling Remote Patient Monitoring Through the Use of Smart Thermostat Data in Canada: Exploratory Study.

BACKGROUND: Advances in technology have made the development of remote patient monitoring possible in recent years. However, there is still room for innovation in the types of technologies that are developed, used, and implemented. The smart thermostat solutions provided in this study can expand beyond typically defined features and be used for improved holistic health monitoring purposes. OBJECTIVE: The aim of this study is to validate the hypothesis that remote motion sensors could be used to quantify and track an individual's movements around the house. On the basis of our results, the next step would be to determine if using remote motion sensors could be a novel data collection method compared with the national census-level surveys administered by governmental bodies. The results will be used to inform a more extensive implementation study of similar smart home technologies to gather data for machine learning algorithms and to build upon pattern recognition and comprehensive health monitoring. METHODS: We conducted a pilot study with a sample size of 8 to validate the use of remote motion sensors to quantify movement in the house. A large database containing data from smart home thermostats was analyzed to compare the following indicators; sleep, physical activity, and sedentary behavior. These indicators were developed by the Public Health Agency of Canada and are collected through traditional survey methods. RESULTS: The results showed a significant Spearman rank correlation coefficient of 0.8 (P<.001), which indicates a positive linear association between the total number of sensors activated and the total number of indoor steps traveled by study participants. In addition, the indicators of sleep, physical activity, and sedentary behavior were all found to be highly comparable with those attained by the Public Health Agency of Canada. CONCLUSIONS: The findings demonstrate that remote motion sensors data from a smart thermostat solution are a viable option when compared with traditional survey data collection methods for health data collection and are also a form of zero-effort technology that can be used to monitor the activity levels and nature of activity of occupants within the home.

Out-of-pocket expenditure and drug adherence of patients with diabetes in Odisha.

INTRODUCTION: The burden of diabetes mellitus (DM) is increasing in India and across states. Given the chronic and progressive nature of the disease, it implicates huge financial burden on patients. Given this, the objectives of this study are to estimate the out-of-pocket (OOP) expenditure on diabetes care and assess the magnitude of medication adherence among patients in a public hospital. MATERIALS AND METHODS: A cross-sectional survey was conducted among 206 patients with age ≥25 years visiting the outpatient department of a tertiary care hospital in Odisha. Cost data were collected from April to June 2016 using a structured questionnaire, and drug adherence was assessed using the Morisky Medication Adherence Scale. RESULTS: The average total expenditure per patient per month was INR 1265 (95% confidence interval 1178-1351), of which medical expenditure was INR 993 (95 confidence interval 912-1075) and that of nonmedical expenditure was INR 271 (95 confidence interval 251-292). Expenditure on medicine constituted around 65% of total medical expenditure. The other drivers of medical expenditure were diagnostics services constituting 13.2% and transportation (11.8%). Overall, only 15% of the patients reported high adherence to medication. DISCUSSION: This study generated evidence on OOP expenditure on diabetics in Odisha which are comparable to many Indian studies. One of the critical findings of this study was that a majority of patients visiting public hospitals had to spend OOP on medicine and diagnostic services. These findings could be used to design appropriate financing strategies to protect the interest of the poor who largely use public health facility in Odisha.

Out-of-Pocket health expenditure and sources of financing for delivery, postpartum, and neonatal health in urban slums of Bhubaneswar, Odisha, India.

BACKGROUND: Out-of-pocket expenditure (OOPE) is an obstacle in the path of getting universal health coverage in India. OBJECTIVE: This study aimed to explore the OOPE, sources of funding, and experience of catastrophic expenditure (CE) for healthcare related to delivery, postpartum, and neonatal morbidity. METHODS: A community-based, cross-sectional survey was conducted among a sample of 240 recently delivered women from the slums of Bhubaneswar, Odisha. Information on background, details of delivery, expenditure on delivery and on morbidities, and sources of funding was collected using a structured interview schedule. RESULTS: Only 29.6% of the households incurred OOPE, and the others incurred either nil OOPE or had a net income because of benefits received from Janani Shishu Suraksha Karyakram (JSSK), Janani Suraksha Yojana (JSY), and "Mamata" schemes of the government. The median total OOPE was found to be 2100 INR (100-38,620). Multivariate analysis found parity, place of delivery, type of delivery, and presence of morbidity to be significantly associated with incurring any OOPE. Nearly 15% of the households incurred OOPE exceeding 40% of the reported monthly household income including 9%, whose OOPE was 100% or more of the reported household monthly income. CONCLUSION: While mechanisms such as JSSK, JSY, and Mamata had benefitted the vast majority, around half of those who did incur OOPE experienced CE. Additional insurance facility for cesarean section delivery might reduce the excessive financial burden on households.

Out of pocket expenditure on surgical and nonsurgical conditions in Odisha.

BACKGROUND: Out of pocket expenditure (OOPE) for any illness is still a major problem in India. Several evidence is available regarding growing OOPE and its impact on household poverty. However, limited evidence is there regarding OOPE on multiple disease conditions in public hospitals. AIM: To estimate the OOPE for various hospitalized conditions at the secondary level of care in Odisha and find out various financial coping mechanisms adopted by the patients. METHODS: The primary survey was done in the secondary care hospitals in the two districts of Odisha using a semi-structured interview schedule. Data were collected from 284 subjects (212 males, 72 females) in 2014 on the socioeconomic status and OOPE on multiple disease conditions. Descriptive statistics using Stata Version 11 were used to estimate the results. RESULTS: The mean total OOPE was Indian Rupees (INR) 2107 (95% confidence interval [CI]: 1788-2426) for single episode of hospitalization out of which medical expenditure was INR 1530 (95% CI: 1238-1821) and nonmedical expenditure was INR 577 (95% CI: 501-653). The OOPE on surgical conditions was 1.7 times more than the nonsurgical conditions. Drugs and diagnostics were the major components of hospital expenditure, whereas the share of transportation expenditure was more in the nonmedical expenditure. Further, most of the patients had to face hardship financing due to limited financial protection measures. CONCLUSIONS: With the growing debate on the rolling out of universal health insurance scheme in India, this study assumes significance by providing critical information for designing public financing strategies to protect the interest of the poor in public health care institutions.

Care seeking pathways of older adults with hip fracture in India: exploratory study protocol.

BACKGROUND: The incidence of hip fractures in older adults in India is likely to increase dramatically in the coming decades as a result of an aging population and increasing life expectancy. Currently, more than 600,000 adults over 60 years of age suffer a hip fracture annually in India. This paper outlines a protocol for a qualitative study investigating the care seeking behavior of older adults with hip fractures: to determine the processes in decision making, identify causes for delay in obtaining care, and identify potential barriers and facilitators to seeking appropriate care in time. METHODS AND DESIGN: The planned study will consider Odisha, an eastern state in India with limited health care facility, as a suitable case study. It is proposed to conduct 30 in-depth interviews in two administrative districts of Odisha. The participants will be patient and their carers in seven health facilities- four public hospitals, two private hospitals and one traditional bone-setting facility. The study relies on a purposive sampling strategy. Ethics permission will be sought from each participating institution and participants. The participants will be adults aged 50 years or older of both sexes arriving at the recruiting centers with a history of fall or injury, pain in the hip region and inability to walk and X-ray confirmed diagnosis of proximal femoral fracture and their primary carer. Trained qualitative research team will conduct these interviews. A thematic framework approach will be used to analyze the data using NVivo 9 software. The data collected from the interviews will be analysed to explore the cause of the hip fracture, events following the injury, the experiences of patients from the time of sustaining the injury, pain relief measures, decision to seek care, understanding of the urgency for treatment, causes for delay in receiving treatment, funding sources, cost liabilities for the family, financing mechanisms for out of pocket expenditure and the burden for caring. DISCUSSION: The findings of this study will provide an increased understanding of the care seeking behaviors of older adults with hip fracture, and inform contextually appropriate changes in healthcare program and policy aimed at improving health outcomes.