The impact of austerity on children: Uncovering effect heterogeneity by political, economic, and family factors in low- and middle-income countries.

Which children are most vulnerable when their government imposes austerity? Research tends to focus on either the political-economic level or the family level. Using a sample of nearly two million children in 67 countries, this study synthesizes theories from family sociology and political science to examine the heterogeneous effects on child poverty of economic shocks following the implementation of an International Monetary Fund (IMF) program. To discover effect heterogeneity, we apply machine learning to policy evaluation. We find that children's average probability of falling into poverty increases by 14 percentage points. We find substantial effect heterogeneity, with family wealth and governments' education spending as the two most important moderators. In contrast to studies that emphasize the vulnerability of low-income families, we find that middle-class children face an equally high risk of poverty. Our results show that synthesizing family and political factors yield deeper knowledge of how economic shocks affect children.

Patterns in the Sequential Treatment of Patients With Rheumatoid Arthritis Starting a Biologic or Targeted Synthetic Disease-Modifying Antirheumatic Drug: 10-Year Experience From a US-Based Registry.

OBJECTIVE: Developing and evaluating new treatment guidelines for rheumatoid arthritis (RA) based on observational data requires a quantitative understanding of patterns in current treatment practice with biologic and targeted synthetic disease-modifying antirheumatic drugs (b/tsDMARDs). METHODS: We used data from the CorEvitas RA registry to study patients starting their first b/tsDMARD therapy, defined as the first line of therapy, between 2012 and the end of 2021. We identified treatment patterns as unique sequences of therapy changes following and including the first-line therapy. Therapy cycling was defined as switching back to a treatment from a previously used therapeutic class. RESULTS: A total of 6015 b/tsDMARD-naïve patients (77% female) were included in the analysis. Their median age was 58 years, and their median disease duration was 3 years. In 2012-2014, 80% of the patients started a tumor necrosis factor inhibitor (TNFi) as their first b/tsDMARD. However, the use of TNFi decreased in favor of Janus kinase inhibitors since 2015. Although the number of treatment patterns was large, therapy cycling was relatively common. For example, 601 patterns were observed among 1133 patients who changed therapy at least four times, of whom 85.3% experienced therapy cycling. Furthermore, the duration of each of the first three lines of therapy decreased over the past decade. For example, the median duration of the first-line therapy was 153 days in 2018-2021 compared to 208 days in 2015-2017 (P < 0.001). CONCLUSION: First-line therapy was almost always TNFi, but diversity in treatment choice was high after that. This practice variation allows for proposing and evaluating new guidelines for sequential treatment of RA. It also presents statistical challenges to compare patients with different treatment sequences.

Assessing clusters of comorbidities in rheumatoid arthritis: a machine learning approach.

BACKGROUND: Comorbid conditions are very common in rheumatoid arthritis (RA) and several prior studies have clustered them using machine learning (ML). We applied various ML algorithms to compare the clusters of comorbidities derived and to assess the value of the clusters for predicting future clinical outcomes. METHODS: A large US-based RA registry, CorEvitas, was used to identify patients for the analysis. We assessed the presence of 24 comorbidities, and ML was used to derive clusters of patients with given comorbidities. K-mode, K-mean, regression-based, and hierarchical clustering were used. To assess the value of these clusters, we compared clusters across different ML algorithms in clinical outcome models predicting clinical disease activity index (CDAI) and health assessment questionnaire (HAQ-DI). We used data from the first 3 years of the 6-year study period to derive clusters and assess time-averaged values for CDAI and HAQ-DI during the latter 3 years. Model fit was assessed via adjusted R2 and root mean square error for a series of models that included clusters from ML clustering and each of the 24 comorbidities separately. RESULTS: 11,883 patients with RA were included who had longitudinal data over 6 years. At baseline, patients were on average 59 (SD 12) years of age, 77% were women, CDAI was 11.3 (SD 11.9, moderate disease activity), HAQ-DI was 0.32 (SD 0.42), and disease duration was 10.8 (SD 9.9) years. During the 6 years of follow-up, the percentage of patients with various comorbidities increased. Using five clusters produced by each of the ML algorithms, multivariable regression models with time-averaged CDAI as an outcome found that the ML-derived comorbidity clusters produced similarly strong models as models with each of the 24 separate comorbidities entered individually. The same patterns were observed for HAQ-DI. CONCLUSIONS: Clustering comorbidities using ML algorithms is not computationally complex but often results in clusters that are difficult to interpret from a clinical standpoint. While ML clustering is useful for modeling multi-omics, using clusters to predict clinical outcomes produces models with a similar fit as those with individual comorbidities.

Patterns in the sequential treatment of rheumatoid arthritis patients starting a b/tsDMARD: 10-year experience from a US-based registry.

Objectives: Developing and evaluating new treatment guidelines for rheumatoid arthritis (RA) based on observational data requires a quantitative understanding of patterns in current treatment practice with biologic and targeted synthetic disease-modifying anti-rheumatic drugs (b/tsDMARDs). Methods: We used data from the CorEvitas RA registry to study patients starting their first b/tsDMARD therapy-defined as the first line of therapy-between 2012 and the end of 2021. We identified treatment patterns as unique sequences of therapy changes following and including the first-line therapy. Therapy cycling was defined as switching back to a treatment from a previously used therapeutic class. Results: 6,015 b/tsDMARD-naive patients (77% female) were included in the analysis. Their median age was 58 years, and their median disease duration was 3 years. In 2012-2014, 80% of the patients started a tumor necrosis factor inhibitor (TNFi) as their first b/tsDMARD. However, the use of TNFi decreased in favour of Janus kinase inhibitors (JAKi) since 2015. While the number of treatment patterns was large, therapy cycling was relatively common. For example, 601 patterns were observed among 1133 patients who changed therapy at least four times, of whom 85.3% experienced therapy cycling. Furthermore, the duration of each of the first three lines of therapy decreased over the past decade. Conclusion: First-line therapy was almost always TNFi, but diversity in treatment choice was high after that. This practice variation allows for proposing and evaluating new guidelines for sequential treatment of RA. It also presents statistical challenges to compare subjects with different treatment sequences.

Predicting progression and cognitive decline in amyloid-positive patients with Alzheimer's disease.

BACKGROUND: In Alzheimer's disease, amyloid- ß (A ß) peptides aggregate in the lowering CSF amyloid levels - a key pathological hallmark of the disease. However, lowered CSF amyloid levels may also be present in cognitively unimpaired elderly individuals. Therefore, it is of great value to explain the variance in disease progression among patients with A ß pathology. METHODS: A cohort of n=2293 participants, of whom n=749 were A ß positive, was selected from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database to study heterogeneity in disease progression for individuals with A ß pathology. The analysis used baseline clinical variables including demographics, genetic markers, and neuropsychological data to predict how the cognitive ability and AD diagnosis of subjects progressed using statistical models and machine learning. Due to the relatively low prevalence of A ß pathology, models fit only to A ß-positive subjects were compared to models fit to an extended cohort including subjects without established A ß pathology, adjusting for covariate differences between the cohorts. RESULTS: A ß pathology status was determined based on the A ß42/A ß40 ratio. The best predictive model of change in cognitive test scores for A ß-positive subjects at the 2-year follow-up achieved an R2 score of 0.388 while the best model predicting adverse changes in diagnosis achieved a weighted F1 score of 0.791. A ß-positive subjects declined faster on average than those without A ß pathology, but the specific level of CSF A ß was not predictive of progression rate. When predicting cognitive score change 4 years after baseline, the best model achieved an R2 score of 0.325 and it was found that fitting models to the extended cohort improved performance. Moreover, using all clinical variables outperformed the best model based only on a suite of cognitive test scores which achieved an R2 score of 0.228. CONCLUSION: Our analysis shows that CSF levels of A ß are not strong predictors of the rate of cognitive decline in A ß-positive subjects when adjusting for other variables. Baseline assessments of cognitive function accounts for the majority of variance explained in the prediction of 2-year decline but is insufficient for achieving optimal results in longer-term predictions. Predicting changes both in cognitive test scores and in diagnosis provides multiple perspectives of the progression of potential AD subjects.

Predicting Response to Tocilizumab Monotherapy in Rheumatoid Arthritis: A Real-world Data Analysis Using Machine Learning.

OBJECTIVE: Tocilizumab (TCZ) has shown similar efficacy when used as monotherapy as in combination with other treatments for rheumatoid arthritis (RA) in randomized controlled trials (RCTs). We derived a remission prediction score for TCZ monotherapy (TCZm) using RCT data and performed an external validation of the prediction score using real-world data (RWD). METHODS: We identified patients in the Corrona RA registry who used TCZm (n = 452), and matched the design and patients from 4 RCTs used in previous work (n = 853). Patients were followed to determine remission status at 24 weeks. We compared the performance of remission prediction models in RWD, first based on variables determined in our prior work in RCTs, and then using an extended variable set, comparing logistic regression and random forest models. We included patients on other biologic disease-modifying antirheumatic drug monotherapies (bDMARDm) to improve prediction. RESULTS: The fraction of patients observed reaching remission on TCZm by their follow-up visit was 12% (n = 53) in RWD vs 15% (n = 127) in RCTs. Discrimination was good in RWD for the risk score developed in RCTs, with area under the receiver-operating characteristic curve (AUROC) of 0.69 (95% CI 0.62-0.75). Fitting the same logistic regression model to all bDMARDm patients in the RWD improved the AUROC on held-out TCZm patients to 0.72 (95% CI 0.63-0.81). Extending the variable set and adding regularization further increased it to 0.76 (95% CI 0.67-0.84). CONCLUSION: The remission prediction scores, derived in RCTs, discriminated patients in RWD about as well as in RCTs. Discrimination was further improved by retraining models on RWD.

The sequence of disease-modifying anti-rheumatic drugs: pathways to and predictors of tocilizumab monotherapy.

BACKGROUND: There are numerous non-biologic and biologic disease-modifying anti-rheumatic drugs (bDMARDs) for rheumatoid arthritis (RA). Typical sequences of bDMARDs are not clear. Future treatment policies and trials should be informed by quantitative estimates of current treatment practice. METHODS: We used data from Corrona, a large real-world RA registry, to develop a method for quantifying sequential patterns in treatment with bDMARDs. As a proof of concept, we study patients who eventually use tocilizumab monotherapy (TCZm), an IL-6 antagonist with similar benefits used as monotherapy or in combination. Patients starting a bDMARD were included and were followed using a discrete-state Markov model, observing changes in treatments every 6 months and determining whether they used TCZm. A supervised machine learning algorithm was then employed to determine longitudinal patient factors associated with TCZm use. RESULTS: 7300 patients starting a bDMARD were followed for up to 5 years. Their median age was 58 years, 78% were female, median disease duration was 5 years, and 57% were seropositive. During follow-up, 287 (3.9%) reported use of TCZm with median time until use of 25.6 (11.5, 56.0) months. Eighty-two percent of TCZm use began within 3 years of starting any bDMARD. Ninety-three percent of TCZm users switched from TCZ combination, a TNF inhibitor, or another bDMARD. Very few patients are given TCZm as their first DMARD (0.6%). Variables associated with the use of TCZm included prior use of TCZ combination therapy, older age, longer disease duration, seronegative, higher disease activity, and no prior use of a TNF inhibitor. CONCLUSIONS: Improved understanding of treatment sequences in RA may help personalize care. These methods may help optimize treatment decisions using large-scale real-world data.

Predicting Remission Among Patients With Rheumatoid Arthritis Starting Tocilizumab Monotherapy: Model Derivation and Remission Score Development.

OBJECTIVE: Most patients with rheumatoid arthritis (RA) strive to consolidate their treatment from methotrexate combinations. The objective of this analysis was to identify patients with RA most likely to achieve remission with tocilizumab (TCZ) monotherapy by developing and validating a prediction model and associated remission score. METHODS: We identified four TCZ monotherapy randomized controlled trials in RA and chose two for derivation and two for internal validation. Remission was defined as a Clinical Disease Activity Index score less than 2.8 at 24 weeks post randomization. We used logistic regression to assess the association between each predictor and remission. After selecting variables and assessing model performance in the derivation data set, we assessed model performance in the validation data set. The cohorts were combined to calculate a remission prediction score. RESULTS: The variables selected included younger age, male sex, lower baseline Clinical Disease Activity Index score, shorter RA disease duration, region of the world (Europe and South America [increased odds of remission] versus Asia and North America), no previous exposure to disease-modifying antirheumatic drugs and/or methotrexate, lower baseline Health Assessment Questionnaire Disability Index score, and baseline hematocrit. The area under the receiver operating characteristic curve was 0.739 in the derivation data set and 0.756 in the validation data set. Patients were categorized into three remission prediction categories based on the remission prediction score: 40% in the low (less than 10% probability of remission), 45% in the intermediate (10%-25% probability), and 15% in the moderate remission prediction category (greater than 25% probability). CONCLUSION: We used easily accessible factors to develop a remission prediction score to predict RA remission at 24 weeks after initializing TCZ monotherapy. These results may provide guidance to clinicians tailoring treatment options based on clinical characteristics.