Determinant Factors of Mortality in Pre-elderly and Elderly Patients With COVID-19 in Jakarta, Indonesia / 예방의학회지

Objectives@#This study aimed to identify risk factors associated with coronavirus disease 2019 (COVID-19) mortality in pre-elderly and elderly individuals in Jakarta, Indonesia. @*Methods@#We employed a case-control study design, utilizing secondary data from the Epidemiology Surveillance, Immunization Prevention, and Disease Control Sections of the DKI Jakarta Provincial Health Office, collected from December 2020 to January 2021. The study included 188 cases and an equal number of controls. Cases were COVID-19 patients confirmed to have died, as reported by hospitals and communities and subsequently verified by healthcare workers. Control subjects were patients who completed a 14-day isolation period and had been officially declared recovered by healthcare professionals. The dependent variable was the mortality of COVID-19 patients in the January 2021 period. The independent variables consisted of demographic data (age and sex), clinical symptoms (cough, runny nose, anosmia, diarrhea, headaches, abdominal pain, muscle pain, and nausea/vomiting), and comorbidities (hypertension, heart disease, and diabetes). Multivariate analysis was conducted using multiple logistic regression. @*Results@#The multiple logistic regression analysis revealed several factors associated with COVID-19 fatalities in Jakarta: age of 60 years or older (odds ratio [OR], 4.84; 95% CI, 3.00 to 7.80), male (OR, 2.38; 95% CI, 2.41 to 3.68), dyspnea (OR, 3.93; 95% CI, 2.04 to 7.55), anosmia (OR, 0.13; 95% CI, 0.04 to 0.46), and heart disease (OR, 4.38; 95% CI, 1.04 to 18.46). @*Conclusions@#The control and prevention of COVID-19 among elderly individuals require particular vigilance. When a COVID-19 case is detected within this demographic, prompt treatment and medication administration are crucial to mitigate the presenting symptoms.

The impact of government pandemic policies on the vulnerability of healthcare workers to SARS-CoV-2 infection and mortality in Jakarta Province, Indonesia.

INTRODUCTION: Healthcare workers (HCWs) are on the frontlines of the COVID-19 pandemic, putting them at a higher risk of infection and disease than non-HCWs. We analysed the effects of government policies for the public and for HCWs on the likelihood of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection and mortality among HCWs during the first 8 months of the pandemic in Jakarta province, the capital city and COVID-19 hotspot in Indonesia. METHODS: We conducted a retrospective cohort study using secondary data from the Jakarta provincial government from March to October 2020, which included sociodemographic characteristics, symptoms, comorbidities and COVID-19 diagnosis history for all cases. A generalized linear mixed-effect regression model was used to determine the effect of each month on the odds ratio (OR) of COVID-19 cases and deaths for HCW compared with non-HCW, assuming that monthly trends were influenced by varying government policies. RESULTS: A total of 894,487 suspected and confirmed COVID-19 cases in health facilities in Jakarta province were analysed. The OR of confirmed cases for HCW was 2.04 (95% CI 2.00-2.08; p < .001) compared to non-HCW. Despite this higher OR for infection, the case fatality rate (2.32 per 100) and OR (1.02, 95% CI 0.93-1.11; p = .65) of COVID-19 deaths for HCW were similar to those of non-HCW. We observed a trend towards a lower number of COVID-19 patients in hospitals and lower odds of COVID-19 cases among HCWs during the April-to-July 2020 phase compared to the August-to-October phase. This chronologically aligned with more extensive policies to support hospital-based, community-based and well-being-related actions to protect HCW. CONCLUSIONS: HCW had higher odds of having SARS-CoV-2 infection, yet similar odds of death from COVID-19, as compared to non-HCW. Government policies with collective efforts to prevent hospital overcapacity during high transmission and burden periods of the pandemic should be prioritized.

Healthcare workers (HCWs) had higher exposure and odds of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection than non-HCWs but a similar risk of death, consistent with previous studies.Government policies favouring reduced workloads of HCW and interventions to promote resilience can be achieved through combined hospital-based, community-based and well-being-related approaches.Studies to identify the patterns and trends of COVID-19 cases and deaths, hospital admissions and policy dynamics are important to promote evidence-based decision-making by the government.

Integration of Hybrid Model Diabetes Self-Management Education and Support at Primary Health Care During COVID-19 Pandemic: Protocol Paper of DIAJAPRI Health Coaching Study

IntroductionCOVID -19 pandemic has threatened the optimal achievement on type-2 diabetes mellitus (T2DM) target in primary health care (PHC), due to our priority in COVID-19 management, limited access of patients to PHC and their lifestyle changes as the impact of social restrictions. Therefore, the empowerment of capability of patients on diabetes self-care is required through optimal education and support. The use of telehealth in T2DM management has benefits on improving outcomes of patients. We aim to assess the role of telehealth diabetes self-management education (DSME) versus hybrid (telehealth and face-to-face method) diabetes self-management education and support (DSMES) to improve T2DM outcomes in PHC during COVID-19 pandemic. Methods and analysisThis study is an open label randomized-controlled trial that will be conducted in 10 PHCs in Jakarta, Indonesia, involving patients with T2DM. Subjects are classified into 2 groups: DSME group and DSMES group. Intervention will be given every 2 weeks. DSME group will receive 1 educational video every 2 weeks discussing topics about diabetes self-management, while DSMES group will receive 1 educational video and undergo 1 coaching session every 2 weeks. All interventions will be conducted by trained health workers of PHC, who are physicians, nurses, and nutritionists. Our primary outcome is the change of HbA1C level and our secondary outcomes are the changes of nutritional intake, physical activity, quality of life, anthropometric parameter, fasting blood glucose, lipid profile, inflammatory markers, and progression of diabetes complications at 3 and 6 months after intervention compare to the baseline. Ethics and disseminationThis study protocol has been approved by the Health Research Ethics Committee University of Indonesia. Subjects agree to participate will be given written informed consent prior to data collection. Findings from this study will be published in peer-reviewed journals and presented at conferences. Trial Registrationhttp://www.clinicalstrials.gov with identifier number NCT05090488. SummaryO_ST_ABSStrengths and limitations of the studyC_ST_ABSO_LIThis study evaluates the role of hybrid DSMES, which is useful in areas with limited access or on lockdowns. C_LIO_LIThis study will evaluates the implementation of hybrid DSMES, its benefits, difficulties, and obstacles. C_LIO_LIWe uses validated questionnaire instruments and routinely collected clinical data. C_LIO_LIBecause all of our interventions will be conducted by PHCs health workers, our results depend on the ability and adherence of PHCs health workers. C_LI

COVID-19 Booster Vaccine Intention by Health Care Workers in Jakarta, Indonesia: Using the Extended Model of Health Behavior Theories.

In Indonesia, COVID-19 vaccination hesitancy persists among health care workers (HCWs). Understanding the motives and challenges impacting HCWs' acceptance of the booster vaccination is critical. Efforts are still needed to overcome apprehension about taking a booster dosage. This study aims to analyze the vaccine acceptance among HCWs in Jakarta using an extended, modified model of health behavior theories, namely The Health Belief Model (HBM) and The Theory of Planned Behavior (TPB). A cross-sectional survey from November 2021 to January 2022 was distributed to health care workers in Jakarta. Bivariate analysis followed by multivariate regression was used to assess factors associated with the vaccine intention and collected 1684 responses. The results have shown that the final model combining the constructs and demographic characteristics could explain 50% of the variance of intention to receive a COVID-19 booster vaccination. Moreover, anticipated regret had the most significant standardized coefficient among the constructs (ß = 0.381, p < 0.001). Other significant predictors in the model were attitude (ß = 0.243, p < 0.001), perceived benefits (ß = 0.103, p < 0.001), subjective norms (ß = 0.08, p = 0.005), and perceived susceptibility (ß = 0.051, p = 0.016). The findings can be used to strategize interventions to increase vaccine uptake.

Pandemic inequity in a megacity: a multilevel analysis of individual, community, and health care vulnerability risks for COVID-19 mortality in Jakarta, Indonesia

BackgroundThe 33 recognized megacities comprise approximately 7% of the global population, yet account for 20% COVID-19 deaths. The specific inequities and other factors within megacities that affect vulnerability to COVID-19 mortality remain poorly defined. We assessed individual, community-level and health care factors associated with COVID-19-related mortality in a megacity of Jakarta, Indonesia, during two epidemic waves spanning March 2, 2020, to August 31, 2021. MethodsThis retrospective cohort included all residents of Jakarta, Indonesia, with PCR-confirmed COVID-19. We extracted demographic, clinical, outcome (recovered or died), vaccine coverage data, and disease prevalence from Jakarta Health Office surveillance records, and collected sub-district level socio-demographics data from various official sources. We used multi-level logistic regression to examine individual, community and sub-district-level health care factors and their associations with COVID-19-mortality. FindingsOf 705,503 cases with a definitive outcome by August 31, 2021, 694,706 (98{middle dot}5%) recovered and 10,797 (1{middle dot}5%) died. The median age was 36 years (IQR 24-50), 13{middle dot}2% (93,459) were <18 years, and 51{middle dot}6% were female. The sub-district level accounted for 1{middle dot}5% of variance in mortality (p<0.0001). Individual-level factors associated with death were older age, male sex, comorbidities, and, during the first wave, age <5 years (adjusted odds ratio (aOR) 1{middle dot}56, 95%CI 1{middle dot}04-2{middle dot}35; reference: age 20-29 years). Community-level factors associated with death were poverty (aOR for the poorer quarter 1{middle dot}35, 95%CI 1{middle dot}17-1{middle dot}55; reference: wealthiest quarter), high population density (aOR for the highest density 1{middle dot}34, 95%CI 1{middle dot}14-2{middle dot}58; reference: the lowest), low vaccine coverage (aOR for the lowest coverage 1{middle dot}25, 95%CI 1{middle dot}13-1{middle dot}38; reference: the highest). InterpretationIn addition to individual risk factors, living in areas with high poverty and density, and low health care performance further increase the vulnerability of communities to COVID-19-associated death in urban low-resource settings. FundingWellcome (UK) Africa Asia Programme Vietnam (106680/Z/14/Z). Research in contextO_ST_ABSEvidence before this studyC_ST_ABSWe searched PubMed on November 22, 2021, for articles that assessed individual, community, and healthcare vulnerability factors associated with coronavirus disease 2019 (COVID-19) mortality, using the search terms ("novel coronavirus" OR "SARS-CoV-2" OR "COVID-19") AND ("death" OR "mortality" OR "deceased") AND ("community" OR "social") AND ("healthcare" OR "health system"). The 33 recognized megacities comprise approximately 7% of the global population, yet account for 20% COVID-19 deaths. The specific inequities and other factors within megacities that affect vulnerability to COVID-19 mortality remain poorly defined. At individual-level, studies have shown COVID-19-related mortality to be associated with older age and common underlying chronic co-morbidities including hypertension, diabetes, obesity, cardiac disease, chronic kidney disease and liver disease. Only few studies from North America, and South America have reported the association between lower community-level socio-economic status and healthcare performance with increased risk of COVID-19-related death. We found no studies have been done to assess individual, community, and healthcare vulnerability factors associated with COVID-19 mortality risk, especially in lower-and middle-income countries (LMIC) where accessing quality health care services is often challenging for substantial proportions of population, due to under-resourced and fragile health systems. In Southeast Asia, by November 22, 2021, COVID-19 case fatality rate had been reported at 2{middle dot}2% (23,951/1,104,835) in Vietnam, 1{middle dot}7% (47,288/2,826,853) in Philippines, 1{middle dot}0% (20,434/2,071,009) in Thailand, 1{middle dot}2% (30,063/2,591,486) in Malaysia, 2{middle dot}4% (2,905/119,904) in Cambodia, and 0{middle dot}3% in Singapore (667/253,649). Indonesia has the highest number of COVID-19 cases and deaths in the region, reporting 3{middle dot}4% case fatality rate (143,744 /4,253,598), with the highest number of cases in the capital city of Jakarta. A preliminary analysis of the first five months of surveillance in Jakarta found that 497 of 4265 (12%) hospitalised patients had died, associated with older age, male sex; pre-existing hypertension, diabetes, or chronic kidney disease; clinical diagnosis of pneumonia; multiple (>3) symptoms; immediate intensive care unit admission, or intubation. Added value of this studyThis retrospective population-based study of the complete epidemiological surveillance data of Jakarta during the first eighteen months of the epidemic is the largest studies in LMIC to date, that comprehensively analysed the individual, community, and healthcare vulnerability associated with COVID-19-related mortality among individuals diagnosed with PCR-confirmed COVID-19. The overall case fatality rate among general population in Jakarta was 1{middle dot}5% (10,797/705,503). Individual factors associated with risk of death were older age, male sex, comorbidities, and, during the first wave, age <5 years (adjusted odds ratio (aOR) 1{middle dot}56, 95%CI 1{middle dot}04-2{middle dot}35; reference: age 20-29 years). The risk of death was further increased for people living in sub-districts with high rates of poverty (aOR for the poorer quarter 1{middle dot}35, 95%CI 1{middle dot}17-1{middle dot}55; reference: wealthiest quarter), high population density (aOR for the highest density 1{middle dot}34, 95%CI 1{middle dot}14-2{middle dot}58), and low COVID-19 vaccination coverage (aOR for the lowest coverage 1{middle dot}25, 95%CI 1{middle dot}13-1{middle dot}38; reference: the highest). Implications of all available evidenceDifferences in socio-demographics and access to quality health services, among other factors, greatly influence COVID-19 mortality in low-resource settings. This study affirmed that in addition to well-known individual risk factors, community-level socio-demographics and healthcare factors further increase the vulnerability of communities to die from COVID-19 in urban low-resource settings. These results highlight the need for accelerated vaccine rollout and additional preventive interventions to protect the urban poor who are most vulnerable to dying from COVID-19.

Geographical heterogeneity and socio-ecological risk profiles of dengue in Jakarta, Indonesia.

The aim of this study was to assess the role of climate variability on the incidence of dengue fever (DF), an endemic arboviral infection existing in Jakarta, Indonesia. The work carried out included analysis of the spatial distribution of confirmed DF cases from January 2007 to December 2018 characterising the sociodemographical and ecological factors in DF high-risk areas. Spearman's rank correlation was used to examine the relationship between DF incidence and climatic factors. Spatial clustering and hotspots of DF were examined using global Moran's I statistic and the local indicator for spatial association analysis. Classification and regression tree (CART) analysis was performed to compare and identify demographical and socio-ecological characteristics of the identified hotspots and low-risk clusters. The seasonality of DF incidence was correlated with precipitation (r=0.254, P<0.01), humidity (r=0.340, P<0.01), dipole mode index (r= -0.459, P<0.01) and Tmin (r= -0.181, P<0.05). DF incidence was spatially clustered at the village level (I=0.294, P<0.001) and 22 hotspots were identified with a concentration in the central and eastern parts of Jakarta. CART analysis showed that age and occupation were the most important factors explaining DF clustering. Areaspecific and population-targeted interventions are needed to improve the situation among those living in the identified DF high-risk areas in Jakarta.

Excess mortality during the first ten months of COVID-19 epidemic at Jakarta, Indonesia

Excess mortality during the COVID-19 epidemic is an important measure of health impacts. We examined mortality records from January 2015 to October 2020 from government sources at Jakarta, Indonesia: 1) burials in public cemeteries; 2) civil death registration; and 3) health authority death registration. During 2015-2019, an average of 26,342 burials occurred each year from January to October. During the same period of 2020, there were 42,460 burials, an excess of 61%. Burial activities began surging in early January 2020, two months before the first official laboratory confirmation of SARS-CoV-2 infection in Indonesia in March 2020. Analysis of civil death registrations or health authority death registration showed insensitive trends during 2020. Burial records indicated substantially increased mortality associated with the onset of and ongoing COVID-19 epidemic in Jakarta and suggest that SARS-CoV-2 transmission may have been initiated and progressing at least two months prior to official detection. Article summary lineAnalysis of civil records of burials in Jakarta, Indonesia showed a 61% increase during 2020 compared to the previous five years, a trend that began two months prior to first official confirmation of SARS-CoV-2 transmission in the city.

Clinical characteristics and mortality associated with COVID-19 in Jakarta, Indonesia: a hospital-based retrospective cohort study

BackgroundData on COVID-19-related mortality and associated factors from low-resource settings are scarce. This study examined clinical characteristics and factors associated with in-hospital mortality of COVID-19 patients in Jakarta, Indonesia, from March 2 to July 31, 2020. MethodsThis retrospective cohort included all hospitalised patients with PCR-confirmed COVID-19 in 55 hospitals. We extracted demographic and clinical data, including hospital outcomes (discharge or death). We used Cox regression to examine factors associated with mortality. FindingsOf 4265 patients with a definitive outcome by July 31, 3768 (88%) were discharged and 497 (12%) died. The median age was 46 years (IQR 32-57), 5% were children, and 31% had at least one comorbidity. Age-specific mortalities were 11% (7/61) for <5 years; 4% (1/23) for 5-9; 2% (3/133) for 10-19; 2% (8/638) for 20-29; 3% (26/755) for 30-39; 7% (61/819) for 40-49; 17% (155/941) for 50-59; 22% (132/611) for 60-69; and 34% (96/284) for [≥]70. Risk of death was associated with higher age; pre-existing hypertension, cardiac disease, chronic kidney disease or liver disease; clinical diagnosis of pneumonia; multiple (>3) symptoms; and shorter time from symptom onset to admission. Patients <50 years with >1 comorbidity had a nearly six-fold higher risk of death than those without (adjusted hazard ratio 5{middle dot}50, 95% CI 2{middle dot}72-11{middle dot}13; 27% vs 3% mortality). InterpretationOverall mortality was lower than reported in high-income countries, probably due to younger age distribution and fewer comorbidities. However, deaths occurred across all ages, with >10% mortality among children <5 years and adults >50 years.

Quantifying the dynamics of COVID-19 burden and impact of interventions in Java, Indonesia

BackgroundAs in many countries, quantifying COVID-19 spread in Indonesia remains challenging due to testing limitations. In Java, non-pharmaceutical interventions (NPIs) were implemented throughout 2020. However, as a vaccination campaign launches, cases and deaths are rising across the island. MethodsWe used modelling to explore the extent to which data on burials in Jakarta using strict COVID-19 protocols (C19P) provide additional insight into the transmissibility of the disease, epidemic trajectory, and the impact of NPIs. We assess how implementation of NPIs in early 2021 will shape the epidemic during the period of likely vaccine roll-out. ResultsC19P burial data in Jakarta suggest a death toll approximately 3.3 times higher than reported. Transmission estimates using these data suggest earlier, larger, and more sustained impact of NPIs. Measures to reduce sub-national spread, particularly during Ramadan, substantially mitigated spread to more vulnerable rural areas. Given current trajectory, daily cases and deaths are likely to increase in most regions as the vaccine is rolled-out. Transmission may peak in early 2021 in Jakarta if current levels of control are maintained. However, relaxation of control measures is likely to lead to a subsequent resurgence in the absence of an effective vaccination campaign. ConclusionSyndromic measures of mortality provide a more complete picture of COVID-19 severity upon which to base decision-making. The high potential impact of the vaccine in Java is attributable to reductions in transmission to date and dependent on these being maintained. Increases in control in the relatively short-term will likely yield large, synergistic increases in vaccine impact. Key questionsO_ST_ABSWhat is already known?C_ST_ABSO_LIIn many settings, limited SARS-CoV-2 testing makes it difficult to estimate the true trajectory and associated burden of the virus. C_LIO_LINon-pharmaceutical interventions (NPIs) are key tools to mitigate SARS-CoV-2 transmission. C_LIO_LIVaccines show promise but effectiveness depends upon prioritization strategies, roll-out and uptake. C_LI What are the new findings?O_LIThis study gives evidence of the value of syndrome-based mortality as a metric, which is less dependent upon testing capacity with which to estimate transmission trends and evaluate intervention impact. C_LIO_LINPIs implemented in Java earlier in the pandemic have substantially slowed the course of the epidemic with movement restrictions during Ramadan preventing spread to more vulnerable rural populations. C_LIO_LIPopulation-level immunity remains below proposed herd-immunity thresholds for the virus, though it is likely substantially higher in Jakarta. C_LI What do the new findings imply?O_LIGiven current levels of control, upwards trends in deaths are likely to continue in many provinces while the vaccine is scheduled to be rolled out. A key exception is Jakarta where population-level immunity may increase to a level where the epidemic begins to decline before the vaccine campaign has reached high coverage. C_LIO_LIFurther relaxation of measures would lead to more rapidly progressing epidemics, depleting the eventual incremental effectiveness of the vaccine. Maintaining adherence to control measures in Jakarta may be particularly challenging if the epidemic enters a decline phase but will remain necessary to prevent a subsequent large wave. Elsewhere, higher levels of control with NPIs are likely to yield high synergistic vaccine impact. C_LI