Background The Coronavirus Disease 2019 (COVID-19) pandemic has led to a significant decline in Non Communicable Diseases (NCD) screening and early detection activities, especially Cardiovascular Disease (CVD). This study aims to assess the potential of community-based self-screening of CVD risk through the mhealth application. Methods This is operational research by actively involving the community to carry out self-screening through the mHealth application. Community health workers were recruited as facilitators who encourage the community to carry out self-screening. To evaluate the potential of community-based self-screening of CVD risk, we use several indicators: responses rate, level of CVD risk, and community acceptance. Results Of the 846 individuals reached by the cadres, 53% or 442 individuals carried out self-screening. Based on the results of self-screening of CVD risk, it is known that around 21.3% are at high risk of developing CVD in the next 10 years. The results of the evaluation of semi-structured questions showed that about 48% of the people had positive impressions, 22% assessed that this self-screening could increase awareness and was informative, 3% suggested improvements to self-screening tools. Conclusion Cadres play an important role in reaching and facilitating the community in their environment to remain aware of their health conditions by conducting self-screening of CVD risk. The availability of the mHealth application that the public can easily access can simplify CVD risk prediction and expand screening coverage, especially during the COVID-19 pandemic, where there are social restrictions policies and community activities.
Introduction Indonesia has not optimally provided complete and reliable civil registration and vital statistics (CRVS). Death certification is one of the elements of the CRVS system. Reliable data on death rates and causes serve as the basis for building a strong evidence base for public health policy, planning, monitoring, and evaluation. This study aims to implement an approach to identifying the cause of death through verbal autopsy by empowering community health workers during the pandemic. Method This study is implementation research with the empowerment of the community, in this case, health cadres and health facilitators/workers, to identify the cause of death through a mobile-based verbal autopsy. This implementation research consisted of four main activities: community-based verbal autopsy, mobile-based verbal autopsy development, data collection, and analysis of the suspected causes of death using InterVA-5. Result From October to November 2020, a total of 143 respondents were willing to do a verbal autopsy interview (response rate of 58%). Of 143 respondents, most of them were women (112 or 78.3%), was the child of the deceased (61 or 42.7%) and lived with the deceased until before he/she died (120 or 83.9%). Based on the characteristics of the deceased, of 143 deceased, 78 (54.5%) were male, 134 (93.7%) were adults, 100 (69.9%) died at home, and 119 (83.2%) did not have a death certificate stating the cause of death. The cause of death of 143 deceased mainly was infectious disease (92 or 64.3%), followed by non-communicable disease (39 or 27.3%), external factors (5 or 3.5%), and unknown factors (4 or 2.8%). In sequence, the top five suspected causes of death are acute respiratory infection, including pneumonia (72 or 50.3%), other and unspecified infectious disease (18 or 12.6%), other and unspecified cardiac disease (17 or 11.9%), acute cardiac disease (4 or 2.8%), and Digestive neoplasms (4 or 2.8%). Conclusion The findings showed that the mobile-based verbal autopsy using a community-based mechanism was feasible during the COVID-19 pandemic.
<div><div><p>Early risk prediction and appropriate treatment are believed to be able to delay the occurrence of hypertension and attendant conditions. Many hypertension prediction models have been developed across the world, but they cannot be generalized directly to all populations, including for Indonesian population. This study aimed to develop and validate a hypertension risk-prediction model using machine learning (ML). The modifiable risk factors are used as the predictor, while the target variable on the algorithm is hypertension status. This study compared several machine-learning algorithms such as decision tree, random forest, gradient boosting, and logistic regression to develop a hypertension prediction model. Several parameters, including the area under the receiver operator characteristic curve (AUC), classification accuracy (CA), F1 score, precision, and recall were used to evaluate the models. Most of the predictors used in this study were significantly correlated with hypertension. Logistic regression algorithm showed better parameter values, with AUC 0.829, CA 89.6%, recall 0.896, precision 0.878, and F1 score 0.877. ML offers the ability to develop a quick prediction model for hypertension screening using non-invasive factors. From this study, we estimate that 89.6% of people with elevated blood pressure obtained on home blood pressure measurement will show clinical hypertension.</p></div></div>
Background The quality of obstetric care has been identified as a contributing factor in Indonesia’s persistently high level of maternal mortality, and the country’s restrictive abortion laws merit special attention to the quality of post-abortion care (PAC). Due to unique health policies and guidelines, in Indonesia, uterine evacuation for PAC is typically administered only by Ob/Gyns practicing in hospitals. Methods Using data from a survey of 657 hospitals and emergency obstetric-registered public health centers in Java, Indonesia’s most populous island, we applied a signal functions analysis to measure the health system’s capacity to offer PAC. We then used this framework to simulate the potential impact of the following hypothetical reforms on PAC capacity: allowing first-trimester uterine evacuation for PAC to take place at the primary care level, and allowing provision by clinicians other than Ob/Gyns. Finally, we calculated the proportion of PAC patients treated using four different uterine evacuation procedures. Results Forty-six percent of hospitals in Java have the full set of services needed to provide PAC, and PAC capacity is concentrated at the highest-level referral hospitals: 86% of referral hospitals have the full set of services, staffing, and equipment compared to 53% of maternity hospitals and 34% of local hospitals. No health centers are adequately staffed or authorized to offer basic PAC services under Indonesia’s current guidelines. PAC capacity at all levels of the health system increases substantially in hypothetical scenarios under which authorization to perform first-trimester uterine evacuation for PAC is expanded to midwives and general physicians practicing in health centers. In 2018, 88% percent of PAC patients were treated using dilation and curettage (D&C). Conclusions Offering first-trimester uterine evacuation for PAC in PONEDs and allowing clinicians other than Ob/Gyns to perform this procedure would greatly improve the capacity of Java’s health system to serve PAC patients. Increasing the use of vacuum aspiration and misoprostol for PAC-related uterine evacuation would lower the burden of treatment for patients and facilitate the task-shifting efforts needed to expand access to this life-saving service.
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