Background Breath tests may diagnose tuberculosis (TB) through detecting specific volatile organic compounds produced by Mycobacterium tuberculosis or the infected host. Methods To estimate the diagnostic accuracy of breath test with electronic-nose and other devices against culture or other tests for TB, we screened multiple databases until January 6, 2019. Findings We included fourteen studies, with 1715 subjects in the analysis. The pooled sensitivity and specificity of electronic-nose were 0.93 (95% CI 0.82–0.97) and 0.93 (95% CI 0.82–0.97), respectively, and no heterogeneity was found. The sensitivity and specificity of other breath test devices ranged from 0.62 to 1.00, and 0.11 to 0.84, respectively. Interpretation The low to moderate evidence of these studies shows that breath tests can diagnose TB accurately, however, to give a real-time test result, additional development is needed. Research should also focus on sputum smear negative TB, children, and the positioning of breath testing in the diagnostic work flow. Funding The authors received no specific funding for this work.

BackgroundAlthough Indonesia has relatively high contraceptive prevalence, postpartum family planning (PP-FP) has not been a particular point of emphasis. This article reports the results of analyses undertaken in order to (1) better understand levels and trends in unmet need for family planning among postpartum women, (2) assess the extent to which unmet need is concentrated among particular population sub-groups, and (3) assess the policy priority that PP-FP should have in relation to other interventions.MethodsThe analyses were based on data from the 2007 and 2012 Indonesia Demographic and Health Surveys (IDHS) and the 2015 PMA2020 survey. Postpartum contraceptive use and unmet need were analyzed for fecund women who had given birth in the 3–5 years of preceding the respective surveys who were in the extended postpartum period at the time of the respective surveys. Factors associated with contraceptive use and unmet were assessed via multivariable logistic regressions using merged data from all three surveys. A wide range of biologic, demographic, socio-economic, geographic and programmatic factors were considered.ResultsContraceptive use during the extended postpartum period is high in Indonesia, with more than 74% of post-partum women reporting currently using a family planning method in the 2015 PMA2020 survey. This is up from 68% in 2007 and 70% in 2012. Total unmet need was 28% in 2007, falling slightly to 23% in 2012 and 24% in 2015. However, the timing of contraceptive initiation is less than optimal. By six months postpartum, only 50% of mothers had begun contraceptive use. Unmet need was highest among older women, women with 4+ children, with limited knowledge of contraceptive methods, making fewer ANC visits, from poor families and residents of islands other than Java and Bali.ConclusionUnmet need for family planning among postpartum women in Indonesia is low in comparison with other low- and middle-income countries. However, because of limited durations of exclusive breastfeeding, many Indonesian women do not initiate contraception early enough after delivering children. Given already high contraceptive prevalence, targeting postpartum women for increased programmatic attention would seem strategically prudent.

BackgroundThe 24-h area under the concentration–time curve (AUC24)/minimal inhibitory concentration ratio is the best predictive pharmacokinetic/pharmacodynamic (PK/PD) parameter of the efficacy of first-line anti-tuberculosis (TB) drugs. An optimal sampling strategy (OSS) is useful for accurately estimating AUC24; however, OSS has not been developed in the fed state or in the early phase of treatment for first-line anti-TB drugs.MethodsAn OSS for the prediction of AUC24 of isoniazid, rifampicin, ethambutol and pyrazinamide was developed for TB patients starting treatment. A prospective, randomized, crossover trial was performed during the first 3 days of treatment in which first-line anti-TB drugs were administered either intravenously or in fasting or fed conditions. The PK data were used to develop OSS with best subset selection multiple linear regression. The OSS was internally validated using a jackknife analysis and externally validated with other patients from different ethnicities and in a steady state of treatment.ResultsOSS using time points of 2, 4 and 8 h post-dose performed best. Bias was < 5% and imprecision was < 15% for all drugs except ethambutol in the fed condition. External validation showed that OSS2-4-8 cannot be used for rifampicin in steady state conditions.ConclusionOSS at 2, 4 and 8 h post-dose enabled an accurate and precise prediction of AUC24 values of first-line anti-TB drugs in this population.Trial RegistrationClinicalTrials.gov (NCT02121314).

In response to the COVID-19 pandemic, mobile-phone data on population movement became publicly available, including Google Community Mobility Reports (CMR). This study explored the utilization of mobility data to predict COVID-19 dynamics in Jakarta, Indonesia. We acquired aggregated and anonymized mobility data sets from 15 February to 31 December 2020. Three statistical models were explored: Poisson Regression Generalized Linear Model (GLM), Negative Binomial Regression GLM, and Multiple Linear Regression (MLR). Due to multicollinearity, three categories were reduced into one single index using Principal Component Analysis (PCA). Multiple Linear Regression with variable adjustments using PCA was the best-fit model, explaining 52% of COVID-19 cases in Jakarta (R-Square: 0.52; p < 0.05). This study found that different types of mobility were significant predictors for COVID-19 cases and have different levels of impact on COVID-19 dynamics in Jakarta, with the highest observed in “grocery and pharmacy” (4.12%). This study demonstrates the practicality of using CMR data to help policymakers in decision making and policy formulation, especially when there are limited data available, and can be used to improve health system readiness by anticipating case surge, such as in the places with a high potential for transmission risk and during seasonal events.

Background Human mobility could act as a vector to facilitate the spread of infectious diseases. In response to the COVID-19 pandemic, Google Community Mobility Reports (CMR) provide the necessary data to explore community mobility further. Therefore, we aimed to examine the relationship between community mobility on COVID-19 dynamics in Jakarta, Indonesia. Methods We utilized the mobility data from Google from February 15 to December 31, 2020. We explored several statistical models to estimate the COVID-19 dynamics in Jakarta. Model 1 was a Poisson Regression Generalized Linear Model (GLM), Model 2 was a Negative Binomial Regression Generalized Linear Model (GLM), and Model 3 was a Multiple Linear Regression (MLR). Results We found that Multiple Linear Regression (MLR) with some adjustments using Principal Component Analysis (PCA) was the best fit model. It explained 52% of COVID-19 cases in Jakarta (R-Square: 0.52, p<0.05). All mobility variables were significant predictors of COVID-19 cases (p<0.05). More precisely, about 1% change in grocery and pharmacy would contribute to a 4.12% increase of the COVID-19 cases in Jakarta. Retails and recreations, workplaces, transit stations, and parks would result in 3.11%, 2.56%, 2.26%, and 1.93% of more COVID-19 cases, respectively. Conclusion Our study indicates that increased mobility contributes to increased COVID-19 cases. This finding will be beneficial to assist policymakers to have better outbreak management strategies, to anticipate increased COVID-19 cases in the future at certain public places and during seasonal events such as annual religious holidays or other long holidays in particular.