Cross-sectional Estimates Research Articles

Calibration and XGBoost reweighting to reduce coverage and non-response biases in overlapping panel surveys: application to the Healthcare and Social Survey

BackgroundSurveys have been used worldwide to provide information on the COVID-19 pandemic impact so as to prepare and deliver an effective Public Health response. Overlapping panel surveys allow longitudinal estimates and more accurate cross-sectional estimates to be obtained thanks to the larger sample size. However, the problem of non-response is particularly aggravated in the case of panel surveys due to population fatigue with repeated surveys.ObjectiveTo develop a new reweighting method for overlapping panel surveys affected by non-response.MethodsWe chose the Healthcare and Social Survey which has an overlapping panel survey design with measurements throughout 2020 and 2021, and random samplings stratified by province and degree of urbanization. Each measurement comprises two samples: a longitudinal sample taken from previous measurements and a new sample taken at each measurement.ResultsOur reweighting methodological approach is the result of a two-step process: the original sampling design weights are corrected by modelling non-response with respect to the longitudinal sample obtained in a previous measurement using machine learning techniques, followed by calibration using the auxiliary information available at the population level. It is applied to the estimation of totals, proportions, ratios, and differences between measurements, and to gender gaps in the variable of self-perceived general health.ConclusionThe proposed method produces suitable estimators for both cross-sectional and longitudinal samples. For addressing future health crises such as COVID-19, it is therefore necessary to reduce potential coverage and non-response biases in surveys by means of utilizing reweighting techniques as proposed in this study.

Insights into nonlinear absorption transitions in a silver-incorporated reduced graphene oxide-molybdenum disulfide (Ag-rGO-MoS2) hybrid.

Optical nonlinearity in a silver-decorated reduced graphene oxide-molybdenum disulfide (Ag-rGO-MoS2) nanocomposite was experimentally investigated via the Z-scan technique using a Q-switched Nd:YAG nanopulsed green laser. An interesting switching behaviour from saturable to reverse saturable absorption with varying input on-axis intensity of the laser was demonstrated. Under low-intensity laser excitation, Ag-rGO-MoS2 displayed ground-state bleaching, which resulted in saturable absorption (SA) behaviour. Here, the prominent SPR phenomenon of Ag enforced plasmon absorption and is assigned to the SA process. Interestingly at higher intensity, the material switched its nonlinearity to reverse saturable absorption (RSA), attributed to two different mechanisms of two-photon absorption (2PA). At moderate laser pulse energies, the sample underwent sequential 2PA, which is underpinned by calculated varying nonlinear optical parameters and excited-state absorption cross-sections. However, at higher intensities, nonlinear absorption coefficients and excited-state absorption cross-sections remained constant, indicating the occurrence of genuine 2PA. In addition, theoretical estimation of the 2PA cross-section validates the observed transition. Thus, experimental evidence for the validity of nonlinear absorption theory (intensity-dependent transition from linear to nonlinear SA to nonlinear RSA to genuine 2PA via sequential 2PA) is provided for the first time in the literature.

Os impactos regionais das mudanças climáticas sobre a produtividade agrícola familiar e patronal no Brasil

Abstract: This paper projects and analyzes the regional impacts of climate change on the agricultural productivity of family farming and large-scale agriculture in Brazil between 2021 and 2050, using the RCP 4.5 and RCP 8.5 scenarios. The methodology adopted consists of a cross-sectional estimation of a production function in which agricultural productivity is determined by climatic, geographic, and productive factors. The study contributes to the literature by disaggregating agricultural production into family farming and large-scale agriculture, indicating the magnitude and direction of impacts by crops and regions in Brazil, a country with a great territorial dimension and relevant and heterogeneous agricultural production. The results indicate that the agricultural productivity of family farming is more sensitive and therefore this type of producer could be more vulnerable to the phenomenon. On average, the effects will be negative in the North/Northeast regions and for cassava, maize, beans, and soybeans, with possible impacts on deforestation and on food supply. Productivity gains are expected in the southern region and for the cultivation of sugar cane and soybeans. Deterioration of food security of the vulnerable farmers and regional disparities may increase in Brazil.

Personal income tax, redistribution and income inequality in Sub-Saharan Africa

We assess the roles of personal income tax and fiscal redistribution in income inequality for 30 Sub-Saharan Africa economies from 1980 to 2020, employing the dynamic common correlated effect and cross-sectional augmented autoregressive distributed lag estimators. Empirical results show that personal income tax positively affects income inequality in the full sample SSA economies. Compared to the full sample, the magnitude of the effect remains positive but smaller for non-least developed countries (countries not classified as least developed countries in our sample). However, personal income tax has a negative effect on income inequality for least developed countries. Additionally, fiscal redistribution increases inequality in Sub-Saharan Africa economies and non-least developed countries, while it lowers inequality for least developed countries. Interestingly, fiscal redistribution reduces the level of the positive impact of personal income tax on inequality over the full sample. The main policy implication of this research is that well-designed redistributive fiscal measures associated with anti-corruption policy and good governance may help policymakers to reduce the positive effect of personal income tax on inequality in Sub-Saharan Africa economies.

Cross-sectional population-based estimates of a rural-urban disparity in prevalence of long COVID among Michigan adults with polymerase chain reaction-confirmed COVID-19, 2020-2022.

To (1) assess whether residential rurality/urbanicity was associated with the prevalence of 30- or 90-day long COVID, and (2) evaluate whether differences in long COVID risk factors might explain this potential disparity. We used data from the Michigan COVID-19 Recovery Surveillance Study, a population-based probability sample of adults with COVID-19 (n = 4,937). We measured residential rurality/urbanicity using dichotomized Rural-Urban Commuting Area codes (metropolitan, nonmetropolitan). We considered outcomes of 30-day long COVID (illness duration ≥30 days) and 90-day long COVID (illness duration ≥90 days). Using Poisson regression, we estimated unadjusted prevalence ratios (PRs) to compare 30- and 90-day long COVID between metropolitan and nonmetropolitan respondents. Then, we adjusted our model to account for differences between groups in long COVID risk factors (age, sex, acute COVID-19 severity, vaccination status, race and ethnicity, socioeconomic status, health care access, SARS-CoV-2 variant, and pre-existing conditions). We estimated associations for the full study period (Jan 1, 2020-May 31, 2022), the pre-vaccine era (before April 5, 2021), and the vaccine era (after April 5, 2021). Compared to metropolitan adults, the prevalence of 30-day long COVID was 15% higher (PR = 1.15 [95% CI: 1.03, 1.29]), and the prevalence of 90-day long COVID was 27% higher (PR = 1.27 [95% CI: 1.09, 1.49]) among nonmetropolitan adults. Adjusting for long COVID risk factors did not reduce disparity estimates in the pre-vaccine era but halved estimates in the vaccine era. Our findings provide evidence of a rural-urban disparity in long COVID and suggest that the factors contributing to this disparity changed over time as the sociopolitical context of the pandemic evolved and COVID-19 vaccines were introduced.

YÜKSELEN PİYASA EKONOMİLERİNDE DOĞRUDAN YABANCI YATIRIMLAR, TİCARİ AÇIKLIK VE CO2 İLİŞKİSİ

In this study, it was aimed to investigate the effect of foreign direct investment (FDI) and trade openness (TO) on environmental pollution. The study was conducted on Emerging Market Economies (EMEs), which are subject to flexible practices and conditions to attract trade and investment. The 1990-2020 data of 15 countries included in the IMF's EMEs category were tested with the following methods: The cross-sectional autoregressive distributed lag estimator (CS ARDL), Main Group (MG), Common Correlated Effects Mean Group Estimators (CCE), Augmented Mean Group (AMG), Emirmahmutoğlu and Köse (2011) Causality Test. The variables used were FDI, TO, GDP per capita and carbon dioxide (CO2) emissions. According to the results of the coefficient estimators, it was found that only GDP had a significant and positive effect on CO2 in both the short and long term, and there was no significant relationship between FDI-CO2 and TO-CO2. According to the causality test results, it was determined that there is a unidirectional relationship from CO2 to FDI and from TO to CO2, and a bidirectional relationship between GDP and CO2.

Reweighting in panel surveys: machine learning techniques for the Health Care and Social Survey

Abstract Background Healthcare statistical services have used probability surveys to respond to such information needs. The Health Care and Social Survey (ESSOC) research project arises from the need to provide data on the evolution of the COVID-19 impact. This survey has an overlapping panel survey design with 4 measurements throughout 1 year with random samples. Objective To develop a new reweighting method for overlapping panel surveys affected by non-response. Methods Each ESSOC measurement is composed of two samples: a longitudinal sample from previous measurements and a new sample at each measurement. In each measurement, missing units are substituted by new surveyed units, allowing the obtention of cross-sectional and longitudinal estimates. The advantage of this design is that, in addition to being able to obtain longitudinal estimates, cross-sectional estimates are more accurate because of the larger sample size. However, the problem of non-response is particularly aggravated in the case of panel surveys, due to the fatigue of the population to be repeatedly surveyed. Results Considering the design, timing and objectives of this survey, our reweighting method produces suitable estimators for both cross-sectional and longitudinal samples. The weights are the result of a two-step process: the original sampling design weights are corrected during a 1st phase by modelling the non-response with respect to the longitudinal sample obtained in a previous measurement using machine learning techniques. Then, during a 2nd phase, they are calibrated using the auxiliary information available at the population level. The proposed method is applied to the estimation of totals, proportions, ratios, and differences between measurements as well as gender gaps. Conclusions For addressing future health crises like COVID-19, it is therefore necessary to reduce potential coverage and non-response biases in surveys by means of utilising reweighting techniques as proposed in this study. Key messages • It is therefore necessary to reduce potential coverage and non-response biases in surveys. • This study proposes the utilisation of reweighting techniques based on calibration and XGBoost.

A New Matrix Statistic for the Hausman Endogeneity Test under Heteroskedasticity

We derive a new matrix statistic for the Hausman test for endogeneity in cross-sectional Instrumental Variables estimation, that incorporates heteroskedasticity in a natural way and does not use a generalized inverse. A Monte Carlo study examines the performance of the statistic for different heteroskedasticity-robust variance estimators and different skedastic situations. We find that the test statistic performs well as regards empirical size in almost all cases; however, as regards empirical power, how one corrects for heteroskedasticity matters. We also compare its performance with that of the Wald statistic from the augmented regression setup that is often used for the endogeneity test, and we find that the choice between them may depend on the desired significance level of the test.

Greening the knowledge-based economies: Harnessing natural resources and innovation in information and communication technologies for green growth

The quest for green growth through the sustainable use of natural resources (NRs) and innovation in information and communication technologies (IICTs) has gained the utmost significance in the modern era, and the knowledge-based economies (KBEs) have been at the leading edge of embracing sustainable methods for accomplishing sustainable development objectives. However, the relationship between NRs, IICTs, and green growth remain unexplored. This study evaluates the impact of IICTs and NRs, alongside other key factors such as contractionary export policy (CEP), expansionary export policy (EEP), fossil fuels consumption (FFC), the labor force (LF), and gross fixed capital formation (GFCF) on green growth. Findings confirm that IICTs, NRs, GFCF, LF, and EEP positively affect green growth. The current research employs Slope homogeneity test, cross-sectional dependency test, Westerlund and Edgerton cointegration test, and cross-sectional augmented autoregressive distributed lag estimator to achieve the study's objective. These results highlight the significance of integrating environmentally conscious procedures and relying on technological advances within the information and communication technology sector to promote green development and ecological integrity. In addition, the study indicates that a growing reliance on FFC and the implementation of CEP harm green growth, underscoring the necessity of moving to renewable energy sources and promoting favorable trade policies. This study suggests that policymakers should emphasize IICTs, effective NRs management, GFCF, labor skills, EEP, and the transition to greener energy. Moreover, they should incorporate ecological factors in their trade practices to reconcile economic growth with sustainability goals.

Catalysts of Economic Welfare in Africa: A Cross-Sectional Autoregressive Distributed Lag Approach

Abstract This study provides empirical perspectives on the catalysts of economic welfare in Africa, drawing inference from macroeconomic and non-macroeconomic factors. Leveraging a sample of a balanced panel dataset of 35 countries across Africa, this study provides novel applications of the cross-sectional autoregressive distributed lag methodology to economic welfare analysis in Africa. Issues of cross-sectional dependence and slope homogeneity were accounted for whilst establishing causal relationships between economic welfare proxied by the Human Development Index and macroeconomic and non-macroeconomic drivers of welfare. Based on cross-sectional autoregressive distributed lag estimation results, a 1% increase in economic growth was shown to account for a 0.233 percent and 0.253 percent increase in economic welfare in the long run and short run respectively. In addition, technology accounted for a 1.81 percent increase in economic welfare in the long run. The outcome of the Dumitrescu–Hurlin causality test demonstrated causality between trade openness, government effectiveness, economic growth, and economic welfare.

Estimation of proton-boron reaction cross-sections by neural networks

The proton-boron fusion reaction is one of the interesting reactions in nuclear energy production. The fact that neutrons and radioactive products do not come out as a result of the reaction makes these reactions special. However, the realization of this reaction is very difficult due to the low reactivity of the plasma and high radiation losses at temperatures achievable in today's fusion devices. Therefore, it is important to determine the cross-sections of these reactions. In our study, we obtained the cross-sections of proton-boron fusion reactions using the machine learning methods after performing machine learning with the limited data available in the literature.

Plasma proteomic signatures of a direct measure of insulin sensitivity in two population cohorts.

The euglycaemic-hyperinsulinaemic clamp (EIC) is the reference standard for the measurement of whole-body insulin sensitivity but is laborious and expensive to perform. We aimed to assess the incremental value of high-throughput plasma proteomic profiling in developing signatures correlating with the M value derived from the EIC. We measured 828 proteins in the fasting plasma of 966 participants from the Relationship between Insulin Sensitivity and Cardiovascular disease (RISC) study and 745 participants from the Uppsala Longitudinal Study of Adult Men (ULSAM) using a high-throughput proximity extension assay. We used the least absolute shrinkage and selection operator (LASSO) approach using clinical variables and protein measures as features. Models were tested within and across cohorts. Our primary model performance metric was the proportion of the M value variance explained (R2). A standard LASSO model incorporating 53 proteins in addition to routinely available clinical variables increased the M value R2 from 0.237 (95% CI 0.178, 0.303) to 0.456 (0.372, 0.536) in RISC. A similar pattern was observed in ULSAM, in which the M value R2 increased from 0.443 (0.360, 0.530) to 0.632 (0.569, 0.698) with the addition of 61 proteins. Models trained in one cohort and tested in the other also demonstrated significant improvements in R2 despite differences in baseline cohort characteristics and clamp methodology (RISC to ULSAM: 0.491 [0.433, 0.539] for 51 proteins; ULSAM to RISC: 0.369 [0.331, 0.416] for 67 proteins). A randomised LASSO and stability selection algorithm selected only two proteins per cohort (three unique proteins), which improved R2 but to a lesser degree than in standard LASSO models: 0.352 (0.266, 0.439) in RISC and 0.495 (0.404, 0.585) in ULSAM. Reductions in improvements of R2 with randomised LASSO and stability selection were less marked in cross-cohort analyses (RISC to ULSAM R2 0.444 [0.391, 0.497]; ULSAM to RISC R2 0.348 [0.300, 0.396]). Models of proteins alone were as effective as models that included both clinical variables and proteins using either standard or randomised LASSO. The single most consistently selected protein across all analyses and models was IGF-binding protein 2. A plasma proteomic signature identified using a standard LASSO approach improves the cross-sectional estimation of the M value over routine clinical variables. However, a small subset of these proteins identified using a stability selection algorithm affords much of this improvement, especially when considering cross-cohort analyses. Our approach provides opportunities to improve the identification of insulin-resistant individuals at risk of insulin resistance-related adverse health consequences.

Exploring the dynamic nexus between urbanization, energy efficiency, renewable energies, economic growth, with ecological footprint: A panel cross-sectional autoregressive distributed lag evidence along Middle East and North Africa countries

The release of greenhouse gases (GHG) is acknowledged as a serious hazard that fuels climate change (CC). The Middle East and North Africa (MENA) countries face particular environmental challenges due to its contribution to carbon dioxide emissions (CO2). There is, however, a scarcity of literature about the region's environmental challenges, despite its vulnerability to the consequences of CC. Therefore, the purpose of this research is to look at various solutions to the CC issue in the MENA area, that is, energy efficiency (EE), economic development, urbanization, fossil fuel (FF) usage, and renewable energies (RE). Further, the study seeks to evaluate CO2 emissions in the region by establishing a RE index. By applying the fresh technique known as the cross-sectional autoregressive distributed lag estimator to panel data for 18 MENA countries from 1990 to 2019, this study quantifies the impact of these variables on CO2 emissions. The findings reveal that the explanatory variables’ significance varies across regional panel clusters, with economic growth having a significantly positive effect on CO2 emissions in the long run. Moreover, the study rejects the Environmental Kuznets Curve hypothesis validation and highlights the importance of EE in boosting environmental quality in the long and short runs. Additionally, the outcomes suggest that RE contributes to reducing CO2 emissions, while the urbanization level and FF consumption adopted by MENA countries do not improve their environment. Thus, mitigating the negative consequences of CC in the MENA area necessitates encouraging EE and lobbying for a shift from FF to RE sources.

Adverse drug reactions and their fatal outcomes in clozapine patients in VigiBase: Comparing the top four reporting countries (US, UK, Canada and Australia)

BackgroundPharmacovigilance findings and box warnings in the clozapine package inserts have marked the history of clozapine. ObjectiveThis is the largest review of clozapine adverse drug reactions (ADRs) and their associated fatal outcomes. Reports to the World Health Organization's global pharmacovigilance database, VigiBase™, were analyzed, extending from clozapine's introduction to December 31, 2022. MethodsThe analysis focused on the top four reporting countries: United States (US), United Kingdom (UK), Canada and Australia (83 % of fatal outcomes worldwide). Attempts were made to control for population and clozapine prescription in each country. ResultsClozapine ADRs worldwide accounted for 191,557 reports, with the highest number (53,505) in “blood and lymphatic system disorder”. Of the 22,596 fatal outcomes reported in clozapine patients, 9587 were from the US, 6567 from the UK, 3623 from Canada and 1484 from Australia. The top category worldwide in fatal outcomes was nonspecifically labeled “death” with 46 % (range 22–62 %). “Pneumonia” was second with 30 % (range 17–45 %). Agranulocytosis was numerically only the 35th top clozapine ADR associated with fatal outcomes. On average, 2.3 clozapine ADRs were reported per fatal outcome. Infections were associated with 24.2 % of the UK fatal outcomes (9.4 %–11.9 % in the 3 other countries). ConclusionsThe four countries appeared to report clozapine ADRs in different ways, making comparisons difficult. We estimated higher fatal outcomes in the UK and Canada after controlling for cross-sectional estimations of population and published clozapine use. This last hypothesis is limited by the lack of precise estimation of accumulated clozapine use in each country.

Rent Seeking, Earning Management and Agency Cost: How Do They Impact Equity Value? Indian Evidence

The article examines the earning management and its nature of the non-rent seeking (NRS) and rent seeking prone (RSP) firms of India followed by their respective valuation implication in the light of agency cost. The various earning management components are estimated from the residuals of a series of regression models, and then compared based on categorization of NRS and rent seeking nature of firms. The robustness of the estimates is validated using various financial characteristics and a separate cross-sectional estimation model. Finally, the article uses ordinary least square (OLS) and 2 stage least square (2 SLS) regression models to find the impact of different earning management components on valuation of NRS and rent seeking firms in the backdrop of their differential agency cost. The results of the study suggest that the cash flow management by the NRS firms with lesser agency cost, is beneficial for equity valuation as the same might convey private information as to future operating cash flow, but accrual management by them impacts valuation negatively—due to information risk. For RSP firms with higher agency cost—cash flow management and cost of production management being opportunistic, inversely impact the equity value. The study provides insight as to how valuation gets impacted by different forms of earning management in presence of differential agency cost of non-rent and rent seekers in an economy where ‘cronyism’ plays a dominant role.

Law and inequality: A comparative approach to the distributive implications of legal systems

The literature on legal traditions focuses on the comparative macroeconomic effects of legal systems, concentrating on efficiency alone and leaving distributive issues to taxation. However, a country’s legal structure also conditions the primary distribution of income and may have a comparative advantage over taxation as a distributive instrument. We use cross-section and panel estimates to show that the level of income inequality in a country is correlated with its legal system. By several measures of inequality, common law countries are on average more unequal than civil law countries. We explain these results by the nature of the systems. The looser regulation in common law countries limits their capacity to achieve social objectives such as combating income inequality.

Wind energy, industrial-economic development and CO2 emissions nexus: Do droughts matter?

This study empirically explored the nexus of wind energy (with regards to the effect of the drought), industrial-economic development, and emitted CO2 in 41 World's top countries in wind energy consumption from 1997 to 2018. Cross-sectional augmented distributed lag estimators (CS-DL, CS-ARDL, CCE-P) and newly updated estimation packages to effectively assess the relationships between variables. Our results are the following: First, severe droughts were not a significant matter in wind energy, and consuming wind energy reasonably contributes to reducing emitted CO2, while industrial and economic development positively promotes CO2 emissions in sampled countries. Second, industrial development significantly promotes economic growth, while wind energy use has an insignificant positive effect on economic growth. Moreover, wind energy negatively affects industrial development. Third, two-way directional causal relationships were noted between CO2 and other covariates, this hypothesis was also noted between industrial development and economic growth and wind energy use. We, therefore, suggested policy implications to reduce CO2 across the globe and country-specific and consider the positive effect of wind energy on growth.

Experimental and theoretical study of the Cu65(n,p)Ni65 reaction cross section from reaction threshold up to 25 MeV

The cross section of the $^{65}\mathrm{Cu}(n,p)^{65}\mathrm{Ni}$ reaction was studied experimentally at three different neutron energies using an activation technique. The quasimonoenergetic neutrons were produced via the $^{7}\mathrm{Li}(p,n)$ reaction at the 14UD BARC-TIFR Pelletron facility in Mumbai, India. Al monitor foils along with Cu samples were activated to determine the incident neutron flux. The activities of the reaction products were measured using a high resolution high purity germanium spectrometry system. Statistical model calculations were performed using the reaction codes talys (ver. 1.9) and empire (ver. 3.2.3) from the reaction threshold to the neutron energy of 25 MeV. Additionally, the effects of various combinations of the theoretical nuclear level densities (NLDs), optical model potentials (OMPs), preequilibrium models (PEs), and \ensuremath{\gamma}-ray strength functions (\ensuremath{\gamma} SFs) were considered for the reproduction of experimental data. The input parameters needed in theoretical calculations to reproduce the present and previous measurements were taken from the RIPL-3 database. The present results are compared with the previous measurements, with the latest evaluations of the ENDF/B-VIII.0, JEFF-3.3, JENDL-4.0/HE, CENDL-3.2, TENDL-2019, and FENDL-3.2 libraries, and with the theoretically calculated values based on talys and empire codes. Furthermore, the cross section of the $^{65}\mathrm{Cu}(n,p)^{65}\mathrm{Ni}$ reaction was estimated within the neutron energies of 14--15 MeV using different systematic formulas. These estimated cross sections by various systematic formulas were compared with the available experimental data. The present data will help to understand the nuclear reaction theory (models) in higher energy regions and improve the evaluated nuclear data evaluation that is needed for fundamental nuclear applications.

Mapping human brain charts cross-sectionally and longitudinally

Brain scans acquired across large, age-diverse cohorts have facilitated recent progress in establishing normative brain aging charts. Here, we ask the critical question of whether cross-sectional estimates of age-related brain trajectories resemble those directly measured from longitudinal data. We show that age-related brain changes inferred from cross-sectionally mapped brain charts can substantially underestimate actual changes measured longitudinally. We further find that brain aging trajectories vary markedly between individuals and are difficult to predict with population-level age trends estimated cross-sectionally. Prediction errors relate modestly to neuroimaging confounds and lifestyle factors. Our findings provide explicit evidence for the importance of longitudinal measurements in ascertaining brain development and aging trajectories.

Methamphetamine exposure and depression-A systematic review and meta-analysis.

Methamphetamine use is a public health concern that has been associated with comorbid mental health problems. We aim to better understand the relationship between methamphetamine use and depression by: (i) systematically reviewing and meta-analysing the risks of depression by methamphetamine use; and (ii) investigating the risk of unmeasured confounding. A systematic review and meta-analysis were conducted following PRISMA guidelines. EMBASE, PsycINFO and PubMed were searched to identify human studies reporting on the association between methamphetamine or amphetamine use and depressive outcomes. The data were summarised narratively and meta-analysed, stratified by cross-sectional and longitudinal estimates. Unmeasured confounding was assessed by E-values analyses. From the 6606 studies that came up from the search, 14 eligible studies were included in the narrative review and had data for meta-analysis. A significant association was found between any use of methamphetamine and any depression outcomes in cross-sectional (odds ratio [OR] = 1.66 [95% confidence interval [CI] 1.34, 2.05]) and longitudinal estimates (OR = 1.18 [95% CI 1.08, 1.28]). People with a methamphetamine use disorder had significantly higher odds of depression than those without (OR = 2.80 [95% CI 1.40, 5.90]). The E-values ranged from 1.28 to 6.30 for cross-sectional studies and from 2.37 to 3.21 for longitudinal studies. Based on limited data, people who used methamphetamine have higher odds of depression than people who do not. There were mostly a low to moderate risk of unmeasured confounding in the longitudinal study results. Future longitudinal studies conducted using causal framework methods are warranted.