Loss To Follow-up Research Articles

Factors associated with treatment outcomes of patients with drug-resistant tuberculosis in China: A retrospective study using competing risk model.

ObjectivesDrug-resistant tuberculosis remains a serious public health problem worldwide, particularly in developing countries, including China. This study determined treatment outcomes among a cohort in Guangzhou, China, and identified factors associated with them.MethodsWe initiated a retrospective study using drug-resistant TB data in Guangzhou from 2016 to 2020, managed by Guangzhou Chest Hospital. A competing risk model was used to identify the factors associated with treatment failure and death, as well as loss to follow-up (LTFU).ResultsA total of 809 patients were included in the study, of which 281 were under treatment. Of the remaining 528 who had clear treatment outcomes, the number and proportion of treatment success, treatment failure, death, and LTFU were 314 (59.5%), 14 (2.7%), 32 (6.0%), and 168 (31.8%), respectively. Being older and having cavities involving the upper lungs were risk factors for treatment failure and death, while non-Guangzhou household registration and interprovincial mobility were risk factors associated with LTFU.ConclusionTreatment failure and death were significantly associated with cavitation in the lungs, and LTFU was significantly associated with household registration and geographical mobility. Early identification of factors associated with different treatment outcomes is extremely important for policymakers, health experts, and researchers to implement appropriate strategies and measures to treat and manage the TB-infected population in China.

Loss to Follow-Up Risk among HIV Patients on ART in Zimbabwe, 2009-2016: Hierarchical Bayesian Spatio-Temporal Modeling.

Loss to follow-up (LTFU) is a risk factor for poor outcomes in HIV patients. The spatio-temporal risk of LTFU is useful to identify hotspots and guide policy. Secondary data on adult HIV patients attending a clinic in provinces of Zimbabwe between 2009 and 2016 were used to estimate the LTFU risk in each of the 10 provinces. A hierarchical Bayesian spatio-temporal Poisson regression model was fitted using the Integrated Nested Laplace Approximation (INLA) package with LTFU as counts adjusting for age, gender, WHO clinical stage, tuberculosis coinfection and duration on ART. The structured random effects were modelled using the conditional autoregression technique and the temporal random effects were modelled using first-order random walk Gaussian priors. The overall rate of LTFU was 22.7% (95%CI: 22.6/22.8) with Harare (50.28%) and Bulawayo (31.11%) having the highest rates. A one-year increase in the average number of years on ART reduced the risk of LTFU by 35% (relative risk (RR) = 0.651; 95%CI: 0.592–0.712). In general, the provinces with the highest exceedance LTFU risk were Matabeleland South and Matabeleland North. LTFU is one of the drawbacks of HIV prevention. Interventions targeting high-risk regions in the southern and northern regions of Zimbabwe are a priority. Community-based interventions and programmes which mitigate LTFU risk remain essential in the global HIV prevention campaign.

Risk Factors Predicting Loss to Follow-Up, Medication Noncompliance, and Poor Visual Outcomes Among Patients With Infectious Keratitis at a Public County Hospital.

Infectious keratitis is a vision-threatening condition requiring close follow-up and disciplined eye drop administration to achieve resolution. Although patients presenting to county hospitals often have more severe presentations, there is a paucity of risk and outcomes data in this setting. This study investigates risk factors predicting loss to follow-up (LTFU), medication noncompliance, and poor outcomes for infectious keratitis in the county hospital setting. This was a retrospective case-control study at Zuckerberg San Francisco General Hospital and Trauma Center. Inclusion criteria were patients who had corneal cultures for suspected infectious bacterial or fungal keratitis between 2010 and 2021. Exclusion criteria were patients with viral keratitis only. Multivariable logistic regression was used to analyze the relationship of social and medical risk factors with LTFU, medication noncompliance, worsened visual acuity (VA), and delayed resolution time. Of 174 patients with infectious keratitis in this analysis, 69 (40.0%) had LTFU. Unemployment was associated with increased risk of LTFU (odds ratio 2.58, P = 0.049) and worse final VA ( P = 0.001). Noncompliance trended toward an association with homelessness (odds ratio 3.48, P = 0.095). Increasing age correlated with longer resolution time, with each 1-year increase associated with delayed resolution by 0.549 days ( P = 0.042). Patients experiencing unemployment, homelessness, or increased age demonstrate higher risk for treatment barriers including loss to follow-up and medication noncompliance, resulting in worse VA and delayed time to resolution. These risk factors should be considered when determining the need for more deliberate follow-up measures in patients with infectious keratitis.

190. Predictions for who are lost to follow-up in spinal cord injury clinical trials: an opportunity to enhance trial design and execution

<h3>BACKGROUND CONTEXT</h3> Despite advancements in research and promising preclinical findings we have yet to isolate any new practice-changing interventions for the treatment of spinal cord injury (SCI) in clinical trials. This raises concerns about the methodology and design of clinical trials in SCI. One of the concerns in SCI trials is with high rates of missing data. Combined with difficulty in recruiting patients and the cost associated with clinical trials in SCI strategies need to be implemented to reduce the rates of loss to follow-up (LTFU) in trials <h3>PURPOSE</h3> Identify factors associated with LTFU in SCI clinical trials. <h3>STUDY DESIGN/SETTING</h3> Multicenter, prospective data from the STASCIS study and the NACTN registry database were analyzed for this study. <h3>PATIENT SAMPLE</h3> All adult patients with SCI from the STASCIS and NACTN registries were selected. <h3>OUTCOME MEASURES</h3> LTFU was defined to be in patients whodid not come to follow up at 12 months and previously did not come to follow-up at least 3 months after baseline ASIA Impairment Scale assessment (≤72 hours post-injury). <h3>METHODS</h3> Missing data were imputed with multiple imputations. Univariate analysis was performed for each independent variable, and predictors with p < 0.2 were included in the final multivariate model for backward stepwise logistic regression based on AIC values. Odds ratios with 95% confidence intervals were calculated with a significance level set as p < 0.05. <h3>RESULTS</h3> A total of 885 patients (46.5±17.6, 45.5% female) were eligible for inclusion. On univariate analysis, 23 variables were significant and included for feature selection. In the final model, patients were less likely to miss follow-up if they had higher baseline lower extremity motor scores (OR: 0.99[0.98-0.99], p=0.021), received early surgery (OR: 0.49[0.35-0.69], p < 0.001), Asian (OR: 0.31[0.18-0.54], p < 0.001) or Black race (OR: 0.63[95% CI 0.45-0.88], p=0.006) compared to White Caucasian, or had a systemic infection complication (OR: 0.35[0.17-0.70], p=0.003). Independent predictors of LTFU were postoperative DVT (OR: 3.28[1.64-6.55], p=0.001) and hematological complications (OR: 1.67[1.07-2.60], p=0.025), and a history of malignancy (OR: 3.65[1.01-13.15], p=0.048) and smoking (OR: 1.55[1.15-2.08], p=0.004). Higher baseline GCS was associated with making follow-up (OR: 0.94[0.88-1.00], p=0.053) and diabetic history with LTFU (OR: 1.56[0.97-2.52], p=0.068), but were not statistically significant in the final model. <h3>CONCLUSIONS</h3> Patients that had a greater motor disability, late surgery and associated comorbidities (such as the history of malignancy, smoking, DVT and hematological complications), are at greater risk for LTFU and may require concerted patient engagement approaches to ensure comprehensive spinal care and track recovery trajectory. <h3>FDA DEVICE/DRUG STATUS</h3> This abstract does not discuss or include any applicable devices or drugs.

The proportion of loss to follow-up from antiretroviral therapy (ART) and its association with age among adolescents living with HIV in sub-Saharan Africa: A systematic review and meta-analysis.

BackgroundHuman immunodeficiency virus (HIV) remains a global health threat, especially in developing countries. The successful scale-up of antiretroviral therapy (ART) programs to address this threat is hindered by a high proportion of patient loss to follow-up (LTFU). LTFU is associated with poor viral suppression and increased mortality. It is particularly acute among adolescents, who face unique adherence challenges. Although LTFU is a critical obstacle on the continuum of care for adolescents, few regional-level studies report the proportion of LTFU among adolescents receiving ART. Therefore, a systematic review and meta-analysis were conducted to estimate the pooled LTFU in ART programs among adolescents living with HIV in sub-Saharan Africa (SSA).MethodsWe searched five databases (PubMed, Embase (Elsevier), PsycINFO, CINAHL, and Scopus) for articles published between 2005 and 2020 and reference lists of included articles. The PRISMA guidelines for systematic reviews were followed. A standardised checklist to extract data was used. Descriptive summaries were presented using narrative tables and figures. Heterogeneity within the included studies was examined using the Cochrane Q test statistics and I2 test. Random effect models were used to estimate the pooled prevalence of LTFU among ALHIV. We used Stata version 16 statistical software for our analysis.ResultsTwenty-nine eligible studies (n = 285,564) were included. An estimated 15.07% (95% CI: 11.07, 19.07) of ALHIV were LTFU. Older adolescents (15–19 years old) were 43% (AOR = 0.57, 95% CI: 0.37, 0.87) more likely to be LTFU than younger (10–14 years old) adolescents. We find an insignificant relationship between gender and LTFU (AOR = 0.95, 95% CI: 0.87, 1.03). A subgroup analysis found that regional differences in the proportion of adolescent LTFU were not statistically significant. The trend analysis indicates an increasing proportion of adolescent LTFU over time.Conclusions and recommendationsThe proportion of LTFU among HIV-positive adolescents in SSA seems higher than those reported in other regions. Older adolescents in the region are at an increased risk for LTFU than younger adolescents. These findings may help policymakers develop appropriate strategies to retain ALHIV in ART services. Such strategies could include community ART distribution points, appointment spacing, adherence clubs, continuous free access to ART, and community-based adherence support.

Risk Factors Associated with Loss to Follow-up Among Transgender Women Receiving HIV Pre-exposure Prophylaxis in Chiang Mai province, Thailand.

Although HIV pre-exposure prophylaxis (PrEP) is free in Thailand, many transgender women discontinue taking it after initiation. We determined the loss to follow-up (LTFU) rate of transgender women who initiated PrEP at the Mplus Foundation, Chiang Mai, Thailand, and identified associated risk factors using Cox proportional hazard models. Of 235 participants who initiated PrEP, 59 (55%) out of 108 remaining participants had reactive syphilis. The LTFU rate at 6 months was 38% (95% confidence interval [CI]: 29–48%). Multivariable analysis indicates that LTFU is independently associated with age ≥ 26 years old (adjusted hazard ratio [aHR] = 2.09; 95% CI: 1.06–4.14) and reactive syphilis (aHR = 1.98; 95% CI:1.01–3.88). Delayed appointment scheduling by the PrEP providers and the syphilis clinic was associated with transgender women having reactive syphilis, and the lockdown policy during the COVID-19 pandemic might have influenced them to discontinue PrEP and their subsequent LTFU.

Incidence and predictors of loss to follow-up among adult HIV-infected patients taking antiretroviral therapy at North Shewa zone public Hospitals, Northeast Ethiopia: A retrospective follow-up study.

Patients who are lost to follow-up while on treatment compromise their own health and the long-term success of antiretroviral therapy (ART) programs. Besides, loss to follow-up (LTFU) increases HIV-related morbidity and mortality. Therefore, this study aimed to determine the incidence of LTFU and its predictors among adult HIV positive patients on anti-retroviral therapy at North Shoa zone public hospitals, Northeast Ethiopia. A retrospective follow up study of 517 people living with HIV/AIDS and attending an ART clinic between 2015 and 2020 was conducted at North Shewa zone, public hospitals. Kaplan-Meier failure function together with log rank test was used to compare failure function. Multivariable Cox proportion hazards regression model was used to determine predictors of LTFU. The incidence density rate of lost to follow up among HIV positive adult on ART was found to be 8.9 per 100 adult years observation (95%CI; 7.45, 10.68). In multivariable cox proportional regression analysis, WHO clinical stage-IV (AHR = 1.50; 95% CI: 1.08, 3.75), comorbidity disease (AHR = 0.54; 95% CI; 0.30, 0.97), body mass index less than 18kg/m2 (AHR = 1.60; 95% CI; 1.02, 2.51), cotrimoxazole preventive therapy (AHR = 1.57; 95% CI;1.09, 2.53), and a low CD4 count (AHR = 1.66; 95% CI; 1.29, 3.49) were found to be a significant predictors of lost to follow up. The current study showed that the incidence rate of loss to ART follow-up was high. Body mass index score less than 18kg/m2, advanced WHO clinical stage, CD4<200cell/mm3, had comorbidity disease, and cotrimoxazole therapy were a significant predictors of lost to ART follow up. Therefore, appropriate mitigation measures in the at-risk group need to be instigated to advance retention rate.

Hidden loss to follow-up among tuberculosis patients managed by public–private mix institutions in South Korea

In South Korea, public–private mix (PPM) was launched in 2011. This retrospective cohort study sought to determine the rate of loss to follow-up (LTFU) among drug-susceptible tuberculosis (DS-TB) patients in all nationwide PPM institutions, and the risk factors for LTFU. National notification data for DS-TB patients diagnosed between August 2011 and July 2014 in PPM institutions were analysed. Determination of LTFU included detection of instances where patients were transferred out, but when they did not attend at other TB centres in the following two months. Univariable and multivariable competing risk models were used to determine risk factors for LTFU. 73,046 patients with 78,485 records were enrolled. Nominally, 3426 (4.4%) of records were LTFU. However, after linking the multiple records in each patient, the percentage of LTFU was 12.3% (9004/73,046). Risk factors for LTFU were: being foreign-born (3.13 (95% CI 2.77–3.53)), prior LTFU (2.31 (2.06–2.59)) and greater distance between the patient’s home and the TB centre (4.27 (4.03–4.53)). ‘Transfer-out’ was a risk factor in patients managed by treatment centres close to home (1.65 (1.49–1.83)), but protective for those attending centres further (0.77 (0.66–0.89)) or far-away (0.52 (0.46–0.59)) from home. By considering the complete picture of a patient’s interactions with healthcare, we identified a much higher level of LTFU than previously documented. This has implications for how outcomes of treatment are reported and argues for a joined-up national approach for the management and surveillance of TB patients, in nations with similar healthcare systems.

Predictors of loss to follow up from antiretroviral therapy among adolescents with HIV/AIDS in Tanzania.

Access to Antiretroviral Therapy (ART) is threatened by the increased rate of loss to follow-up (LTFU) among adolescents on ART care. We investigated the rate of LTFU from HIV care and associated predictors among adolescents living with HIV/AIDS in Tanzania. A retrospective cohort analysis of adolescents on ART from January 2014 to December 2016 was performed. Kaplan-Meier method was used to determine failure probabilities and the Cox proportion hazard regression model was used to determine predictors of loss to follow up. A total of 25,484 adolescents were on ART between 2014 and 2016, of whom 78.4% were female and 42% of adolescents were lost to follow-up. Predictors associated with LTFU included; adolescents aged 15-19 years (adjusted hazard ratio (aHR): 1.57; 95% Confidence Interval (CI); 1.47-1.69), having HIV/TB co-infection (aHR: 1.58; 95% CI, 1.32-1.89), attending care at dispensaries (aHR: 1.12; 95% CI, 1.07-1.18) or health center (aHR: 1.10; 95% CI, 1.04-1.15), and being malnourished (aHR: 2.27; 95% CI,1.56-3.23). Moreover, residing in the Lake Zone and having advanced HIV disease were associated with LTFU. These findings highlight the high rate of LTFU and the need for intervention targeting older adolescents with advanced diseases and strengthening primary public facilities to achieve the 2030 goal of ending HIV as a public health threat.

Hepatitis C treatment outcomes for Australian First Nations Peoples: equivalent SVR rate but higher rates of loss to follow-up

BackgroundFirst Nations Peoples of Australia are disproportionally affected by hepatitis C (HCV) infection. Through a prospective study we evaluated the outcome of direct-acting antiviral (DAA) therapy among First Nations Peoples with HCV infection.MethodsAdults who initiated DAA therapy at one of 26 hospitals across Australia, 2016–2019 were included in the study. Clinical data were obtained from medical records and the Pharmaceutical and Medicare Benefits Schemes. Outcomes included sustained virologic response (SVR) and loss to follow-up (LTFU). A multivariable analysis assessed factors associated with LTFU.ResultsCompared to non-Indigenous Australians (n = 3206), First Nations Peoples (n = 89) were younger (p < 0.001), morel likely to reside in most disadvantaged (p = 0.002) and in regional/remote areas (p < 0.001), and had similar liver disease severity. Medicines for mental health conditions were most commonly dispensed among First Nations Peoples (55.2% vs. 42.8%; p = 0.022). Of 2910 patients with follow-up data, both groups had high SVR rates (95.3% of First Nations Peoples vs. 93.2% of non-Indigenous patients; p = 0.51) and ‘good’ adherence (90.0% vs. 86.9%, respectively; p = 0.43). However, 28.1% of First Nations Peoples were LTFU vs. 11.2% of non-Indigenous patients (p < 0.001). Among First Nations Peoples, younger age (adj-OR = 0.93, 95% CI 0.87–0.99) and treatment initiation in 2018–2019 vs. 2016 (adj-OR = 5.14, 95% CI 1.23–21.36) predicted LTFU, while higher fibrosis score was associated with better engagement in HCV care (adj-OR = 0.71, 95% CI 0.50–0.99).ConclusionsOur data showed that First Nations Peoples have an equivalent HCV cure rate, but higher rates of LTFU. Better strategies to increase engagement of First Nations Peoples with HCV care are needed.

Improving the Diagnosis of Tuberculosis through Optimization of Sputum Microscopy

Background: Patient compliance is an important component of the case detection. The feasibility of sputum examination on the same day was assessed without compromising on the quality. Materials and Methods: This study was conducted as a cross-sectional study on 215 patients attending the DOTS center in Dr. Rajendra Prasad Government Medical College, Kangra, at Tanda from July 2018 to December 2018. The patients were divided into three arms of the study based on the timing of the sputum sample. Results: There was a high correlation of the test result across all study groups. Combined for all arms, the correlation between the first spot and second spot sputum sample was 0.87, between the first spot and morning sputum sample was 0.82, and between the second spot and morning sputum sample was 0.85. There was no significant statistical difference in the sputum positivity among the groups. The loss to follow-up (LFU) was high (21.9%) for the second and third spot (early morning) samples in group 2, and no LFU was observed for the second spot (after 1 h) and morning sample in group 1 patients. Conclusion: The combination of the first and early morning samples offers a feasible mechanism to diagnose tuberculosis (TB) based on acid fast bacillus identification. Establishing the diagnosis for TB based on two sample results is feasible without any significant drop in the yield.

Predicting the loss to follow-up (LTFU) of HIV/AIDS patients in China using a recency-frequency (RF) model.

We constructed a recency-frequency (RF) model for predicting the loss to follow-up (LTFU) in HIV/AIDS patients in China. Data on HIV/AIDS outpatients in the research unit from 1 August 2009 to 30 September 2020 and from 1 October to 31 December 2020 were exported as the observation and prediction datasets, respectively. The classic recency-frequency-monetary (RFM) model was expanded into RFm, RF, RFL and RFmL models. In the observation dataset, the best predictive model was obtained using k-means clustering and C5.0 verification. Then, two rounds of k-means modelling were performed on the best model: data with R ≤ 6months were retained, randomly divided into a training set (70%) and a testing set (30%) and used to perform the second round of modelling to subdivide the churn and non-churn patients. Next, an ANN algorithm was used to predict LTFU, and the confusion matrix with prediction datasets was constructed. The observation and prediction datasets included 16 949 and 10 748 samples, respectively. The RF model with three clusters and a quality of 0.82 was the best predictive model. From the observation set, 13 799 samples were retained, and the model accuracy was 100% on the training and testing sets. These 13 799 samples were subdivided into 1563 samples of churn patients and 12 216 samples of non-churn patients. The accuracy of ANN prediction was 99.89%. The accuracy and precision of the confusion matrix were 85.41% and 99.76%, respectively. The RF model is effective in predicting the LTFU of HIV/AIDS patients in China and preventing its occurrence.

Loss to Follow-up and Unplanned Readmission After Emergent Surgery for Acute Subdural Hematoma

Loss to follow-up (LTF) and unplanned readmission are barriers to recovery after acute subdural hematoma evacuation. The variables associated with these postdischarge events are not fully understood. To determine factors associated with LTF and unplanned readmission, emphasizing socioeconomic status (SES). A retrospective analysis was conducted of surgical patients with acute subdural hematoma managed operatively from 2009 to 2019 at a level 1 regional trauma center. Area Deprivation Index (ADI), which is a neighborhood-level composite socioeconomic score, was used to measure SES. Higher ADI corresponds to lower SES. To decrease the number of covariates in the model, principal components (PCs) analysis was used. Multivariable logistic regression analyses of PCs were performed for LTF and unplanned readmission. A total of 172 patients were included in this study. Thirty-six patients (21%) were LTF, and 49 (28%) patients were readmitted; 11 (6%) patients were both LTF and readmitted ( P = .9). The median time to readmission was 10 days (Q1: 4.5, Q3: 35). In multivariable logistic regression analyses for LTF, increased ADI and distance to hospital through PC2 (odds ratio [OR] 1.49; P = .009) and uninsured/Medicaid status and increased length of stay through PC4 (OR 1.73; P = .015) significantly contributed to the risk of LTF. Unfavorable discharge functional status and nonhome disposition through PC3 were associated with decreased odds of unplanned readmission (OR = 0.69; P = .028). Patients at high risk for LTF and unplanned readmissions, as identified in this study, may benefit from targeted resources individualized to their needs to address barrier to follow-up and to ensure continuity of care.

Predictors of Missed Follow-up Visits in the National Traumatic Brain Injury Model Systems Cohort Study

ObjectiveTo identify key variables that could predict risk of loss to follow-up (LTFU) in a nationally funded longitudinal database of persons with traumatic brain injury. DesignSecondary analysis of a prospective longitudinal cohort study. SettingTraumatic Brain Injury Model System (TBIMS) Centers in the US. ParticipantsA total of 17,956 TBIMS participants (N=17,956) with interview status data available were included if eligible for 1-, 2-, 5-, 10-, 15-, or 20-year follow-ups between October 31, 1989, and September 30, 2020. InterventionsNot applicable. Main Outcome MeasuresFollow-up data collection completion status at years 1, 2, 5, 10, 15, and 20. ResultsInformation relevant to participants’ history, injury characteristics, rehabilitation stay, and patterns of follow-up across 20 years were considered using a series of logistic regression models. Overall, LTFU rates were low (consistently <20%). The most robust predictors of LTFU across models were missed earlier follow-ups and demographic factors including Hispanic ethnicity, lower education, and lack of private health insurance. ConclusionsEfforts to retain participants in such social disadvantaged or minority groups are encouraged given their disproportionate rate of LTFU. Repeated attempts to reach participants after a previously missed assessment are beneficial because many participants that missed 1 or more follow-ups were later recovered.

Survival probability and factors associated with time to loss to follow-up and mortality among patients on antiretroviral treatment in central Kenya

BackgroundRetention of patients who are receiving antiretroviral therapy (ART) remains a challenge especially in the setting of rapid expansion of HIV services. Retention in care remains vital to the HIV care continuum, and has been associated with viral suppression and improved survival. This study aimed to ascertain survival rates, time to loss to follow-up (LTFU) or mortality events and factors associated with time to LTFU or mortality among patients enrolled on antiretroviral therapy at health facilities in central Kenya.MethodsThis was a retrospective cohort study among patients initiated on ART between 2004 and 2012 in central Kenya. Demographic characteristics, clinical characteristics and outcomes data were analyzed using Stata version 15.1. Competing risks regression analysis and cummulative incidence functions were used to estimate survival.ResultsA total of 31,346 patients were included, of whom 65.6% were female, 76.0% were aged between 20 and 50 years old, and 38.9% were diagnosed at WHO stage III. At 36 months, overall retention was 68.8%, LTFU was 27.1%, and mortality was 4.1%. The total person-years of follow up was 74,986. The incidence rate of LTFU was 9.99 per 100 person years for a total of 9383.25 person-years of follow up. The mortality rate was 1.25 per 100 person years for a total of 875.5 person-years among those who died. The median time to LTFU was 11 months (IQR 3–22) while median time to death was 3 months (IQR 0–13). Men, unmarried patients, patients presenting with advanced HIV, not on TB treatment, and enrolled into the HIV program in later cohorts, had a shorter time to mortality and LTFU.ConclusionOur study demonstrated evidence of scale-up of HIV treatment programs in central Kenya. While most patients were enrolled at an advanced WHO clinical stage, overall 36-month mortality remained low, but occurred earlier during follow-up. Cohort LTFU at 36-months reduced in later years with the losses occurring within the 1st year of follow-up. Predictors of early mortality and LTFU included being male, single, separated or divorced, advanced WHO clinical stage, and among patients not on TB treatment.

1007-P: Health Disparities in the Effect of the COVID-Pandemic on Loss-to-Follow-Up Rates among High-Risk Pediatric Patients with Type 1 Diabetes (T1D)

Objective: Historically marginalized groups have higher rates of poor glycemic control linked to barriers in access to care. We sought to examine whether there was a differential effect of the COVID-pandemic on loss-to-follow-up (LTFU) rates among high-risk youth with T1D based on language for care, race, and ethnicity. Methods: Retrospective cohort study of pediatric patients with T1D (HbA1c ≥9%) seen at a tertiary care diabetes clinic between 1/1/18-2/29/20 (pre-COVID period) and 3/1/20-1/31/21 (COVID period) . LTFU was tracked monthly and defined as patients not seen in the preceding 6 months. Preferred language for care, race, and ethnicity data were obtained from visit registration data. Generalized estimating equations were used to examine associations between time-periods (pre-COVID versus COVID) and LTFU rates, adjusted for patient age. In two models, separate interaction terms for (1) language for care and (2) race and ethnicity were introduced to assess for effect modification. Results: 1359 patients were included (9.5% with language other than English [LOE]; 33.5% from historically marginalized racial and ethnic groups) . We found a 1.27 higher odds (95% CI: 1.08, 1.49) of LTFU after the onset of the pandemic, with differential effects based on language for care. Among patients with LOE, the odds of LTFU in the COVID period was 4.14 higher (95% CI: 2.35, 7.29) compared to the pre-COVID period. LTFU rates did not significantly differ between time periods among English-speaking patients (OR 1.12 [95% CI: 0.95, 1.33]) . Patient’s race and ethnicity did not modify the effect of the COVID-pandemic on LTFU rates. Conclusion: The COVID-pandemic has exacerbated LTFU rates in high-risk youth with T1D and disproportionately impacted patients whose preferred language for care is a LOE. Interventions to improve LTFU rates should include targeted efforts to reduce health disparities in access to care in this high-risk population. Disclosure M.Gupta: None. K.Carlin: None. A.J.Roberts: None. F.Malik: n/a. A.Desai: None.

Cascade of care among hepatitis B patients in Maastricht, the Netherlands, 1996 to 2018

Background & aimsThere are approximately 49,000 people (0.34%) in the Netherlands with a chronic hepatitis B virus (HBV) infection. It is unclear how many are linked to care and under follow-up in hepatitis outpatient clinics. This study determined the cascade of care and identified predictors for not being linked to care and loss to follow-up in Maastricht, the Netherlands. MethodsAll hepatitis B surface antigen (HBsAg)-positive patients between December 1, 1996 and September 30, 2018 were retrospectively identified. ResultsIn total, 644 HBsAg-positive patients were identified; of whom 75 had acute HBV infection, 471 chronic HBV infection and 98 unknown. Out of 569 individuals with a chronic/unknown HBV status, 134/569 (23.6%) were not linked to care and 58.7% (195/332 after excluding those who died or achieved HBsAg-seroclearance) were loss to follow-up (LTFU). A predictor for not being linked to care was Caucasian ethnicity (odds ratio (OR) = 2.76 (95% Confidence Interval (CI) = 1.21–6.29); p = .015). Predictors for LTFU were older age (OR = 0.97 (CI = 0.94–0.99); p = .008), HBV DNA >20,000 IU/mL (OR = 0.44 (CI = 0.21 - 0.93); p = .033) and Asian ethnicity (OR = 0.46, (CI = 0.21–1.00); p = .050). Rates of not being linked to care and LTFU decreased over time from 12.7% in 1996 to 4.4% in 2018 and from 79.2% in 1996 to 37.2% in 2018, respectively. ConclusionsA considerable amount of HBsAg-positive individuals were not linked to care or LTFU. This study indicates that ethnicity plays a role in linkage to care and follow-up. Further research is needed to elaborate on those results.

Predictive Analytics Using Machine Learning to Identify ART Clients at Health System Level at Greatest Risk of Treatment Interruption in Mozambique and Nigeria.

A core objective of HIV/AIDS programming is keeping clients on treatment to improve their health outcomes and to limit spread. Machine learning and artificial intelligence can combine client, temporal, and locational attributes to identify which clients are at greatest risk of loss to follow-up (LTFU) and enable health providers to direct support interventions accordingly. The analysis was part of a project funded by U.S. President's Emergency Plan for AIDS Relief and United States Agency for International Development, Data for Implementation, and applied to data from publicly available sources (health facility data, geospatial data, and satellite imagery) and de-identified electronic medical record data on antiretroviral therapy clients in Nigeria and Mozambique. The project applied binary classification techniques using temporal cross-validation to predict the risk that patients would be LTFU. Classifiers included logistic regression, neural networks, and tree-based models. Models showed strong predictive power in both settings. In Mozambique, the best-performing model, a Random Forest, achieved an area under the precision-recall curve of 0.65 compared against an underlying LTFU rate of 23%. In Nigeria, the best-performing model, a boosted tree, achieved an area under the precision-recall curve of 0.52 compared against an underlying LTFU rate of 27%. Machine-learned models outperformed current classification techniques and showed potential to better direct health worker resources toward patients at greatest risk of LTFU. Moreover, models performed equally across sex and age groups, supporting the model's generalizability and wider application.

Predictors of Loss to Follow-Up in an HIV Vaccine Preparedness Study in Masaka, Uganda.

Background: High participant retention is essential to achieve adequate statistical power for clinical trials. We assessed participant retention and predictors of loss to follow-up (LTFU) in an HIV vaccine-preparedness study in Masaka, Uganda. Methods: Between July 2018 and March 2021, HIV sero-negative adults (18–45 years) at high risk of HIV infection were identified through HIV counselling and testing (HCT) from sex-work hotspots along the trans-African highway and fishing communities along the shores of Lake Victoria. Study procedures included collection of baseline socio-demographic data, quarterly HCT, and 6-monthly collection of sexual risk behaviour data. Retention strategies included collection of detailed locator data, short clinic visits (1–2 h), flexible reimbursement for transport costs, immediate (≤7 days) follow-up of missed visits via phone and/or home visits, and community engagement meetings. LTFU was defined as missing ≥2 sequential study visits. Poisson regression models were used to identify baseline factors associated with LTFU. Results: 672 participants were included in this analysis. Of these, 336 (50%) were female and 390 (58%) were ≤24 years. The median follow-up time was 11 months (range: 0–31 months). A total 214 (32%) participants were LTFU over 607.8 person-years of observation (PYO), a rate of 35.2/100 PYO. LTFU was higher in younger participants (18–24 years versus 35–45 years, adjusted rate ratio (aRR) = 1.29, 95% confidence interval (CI) 0.80–2.11), although this difference was not significant. Female sex (aRR = 2.07, 95% CI, 1.51–2.84), and recreational drug use (aRR = 1.61, 95% CI, 1.12–2.34) were significantly associated with increased LTFU. Engagement in transactional sex was associated with increased LTFU (aRR = 1.36, 95% CI, 0.97–1.90) but this difference was not significant. LTFU was higher in 2020–2021 (the period of COVID-19 restrictions) compared to 2018–2019 (aRR = 1.54, 1.17–2.03). Being Muslim or other (aRR = 0.68, 95% CI 0.47–0.97) and self-identification as a sex worker (aRR = 0.47, 95% CI, 0.31–0.72) were associated with reduced LTFU. Conclusion: We observed a high LTFU rate in this cohort. LTFU was highest among women, younger persons, recreational drug users, and persons who engage in transactional sex. Efforts to design retention strategies should focus on these subpopulations.

"Better to Die Than Take These Medicines": A Community-Based Qualitative Study on the Determinants of Treatment Loss-to-Follow-Up in Tuberculosis Patients in District Faridabad, Haryana, India.

IntroductionIndia is the biggest contributor to the global incidence of tuberculosis (TB). A major reason behind the persistently high incidence of TB in India is treatment loss-to-follow-up (LTFU). The consequences of LTFU include continuous transmission to uninfected individuals, drug resistance, and a higher rate of death in incompletely treated patients. It is a significant hurdle to making India ‘TB-Free’ by 2025. Hence, we conducted a community-based qualitative study to understand the determinants of treatment of LTFU in TB patients in the Faridabad district of Haryana, India. MethodologyWe enrolled TB patients who had completed treatment as well as those who had been LTFU. We also enrolled National Tuberculosis Elimination Programme (NTEP) functionaries, healthcare providers, family members, and community members. In-depth interviews (IDIs) and focus group discussions (FGDs) were conducted to understand stakeholders’ perceptions of reasons for LTFU. The grounded theory approach was used with inductive analysis. Data were triangulated from stakeholders’ interviews. Themes and sub-themes were identified. A Health Belief Model for TB treatment completion was developed.ResultsFifty-eight IDIs and four FGDs were conducted between May-June 2018. The major themes influencing the treatment of LTFU which emerged from the analyses were - the role of external motivators, regular use of alcohol, lack of/or inappropriate knowledge related to treatment, lack of family support, and side effects of anti-tubercular drugs, and a poor experience with the health system. Stigma was not found to be a major determinant - in the few cases that it affected treatment, it spurred treatment completion rather than LTFU.“I completed the course with great difficulty. Then they started it again! […] I said-Sorry, sir, I can’t go through this again. It’s better to die once than to die a thousand deaths.” - Fifty-one-year-old male patient who was lost-to-follow-up on re-treatment.DiscussionThis study was a comprehensive multi-stakeholder qualitative undertaking to identify the determinants of LTFU. Our qualitative approach explained the associations between LTFU and certain factors (e.g.: alcohol use, side effects, etc.) found in previous quantitative studies. The strength of this study was that we ensured participation by patients as well as all district-level stakeholders from the national health programme, which no previous qualitative study on the treatment LTFU in India had achieved. The entire qualitative analysis was done manually and in Hindi (the language in which interviews were conducted). Hence, no data were lost in translation. The limitation was that its findings were specific to the study area and study population, as is the case with all qualitative studies.ConclusionAll healthcare providers should be sensitised to the determinants of treatment LTFU, so that they can pay special attention to at-risk patients and take appropriate steps to prevent LTFU. For instance, patients with a pattern of regular alcohol use should be counselled and may be referred to deaddiction centres, with the continuum of care maintained. The journey from tuberculosis diagnosis to treatment completion is often extremely traumatic for the patient. The onus to successfully complete treatment lies not with the patient alone, but with the health system as well.