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On average, small business support projects appear to improve beneficiary sales, and the growth effect is obvious when supporting young or growing firms. However, the effect is largely offset by sales reductions due to overcrowding. Small business support projects must be operated in two ways to alleviate the overcrowding of businesses in a few industries and to enhance the overall effectiveness of the support programs.

BackgroundThe Harmonized Diagnostic Assessment of Dementia for the Longitudinal Aging Study in India (LASI-DAD) is the first and only nationally representative study on late-life cognition and dementia in India (n=4096). LASI-DAD obtained clinical consensus diagnosis of dementia for a subsample of 2528 respondents. ObjectiveThis study develops a machine learning model that uses data from the clinical consensus diagnosis in LASI-DAD to support the classification of dementia status. MethodsClinicians were presented with the extensive data collected from LASI-DAD, including sociodemographic information and health history of respondents, results from the screening tests of cognitive status, and information obtained from informant interviews. Based on the Clinical Dementia Rating (CDR) and using an online platform, clinicians individually evaluated each case and then reached a consensus diagnosis. A 2-step procedure was implemented to train several candidate machine learning models, which were evaluated using a separate test set for predictive accuracy measurement, including the area under receiver operating curve (AUROC), accuracy, sensitivity, specificity, precision, F1 score, and kappa statistic. The ultimate model was selected based on overall agreement as measured by kappa. We further examined the overall accuracy and agreement with the final consensus diagnoses between the selected machine learning model and individual clinicians who participated in the clinical consensus diagnostic process. Finally, we applied the selected model to a subgroup of LASI-DAD participants for whom the clinical consensus diagnosis was not obtained to predict their dementia status. ResultsAmong the 2528 individuals who received clinical consensus diagnosis, 192 (6.7% after adjusting for sampling weight) were diagnosed with dementia. All candidate machine learning models achieved outstanding discriminative ability, as indicated by AUROC >.90, and had similar accuracy and specificity (both around 0.95). The support vector machine model outperformed other models with the highest sensitivity (0.81), F1 score (0.72), and kappa (.70, indicating substantial agreement) and the second highest precision (0.65). As a result, the support vector machine was selected as the ultimate model. Further examination revealed that overall accuracy and agreement were similar between the selected model and individual clinicians. Application of the prediction model on 1568 individuals without clinical consensus diagnosis classified 127 individuals as living with dementia. After applying sampling weight, we can estimate the prevalence of dementia in the population as 7.4%. ConclusionsThe selected machine learning model has outstanding discriminative ability and substantial agreement with a clinical consensus diagnosis of dementia. The model can serve as a computer model of the clinical knowledge and experience encoded in the clinical consensus diagnostic process and has many potential applications, including predicting missed dementia diagnoses and serving as a clinical decision support tool or virtual rater to assist diagnosis of dementia.

Asian Americans (AsA), Native Hawaiians, and Pacific Islanders (NHPI) comprise 7.7% of the U.S. population, and AsA have had the fastest growth rate since 2010. Yet the National Institutes of Health (NIH) has invested only 0.17% of its budget on AsA and NHPI research between 1992 and 2018. More than 40 ethnic subgroups are included within AsA and NHPI (with no majority subpopulation), which are highly diverse culturally, demographically, linguistically, and socioeconomically. However, data for these groups are often aggregated, masking critical health disparities and their drivers. To address these issues, in March 2021, the National Heart, Lung, and Blood Institute, in partnership with 8 other NIH institutes, convened a multidisciplinary workshop to review current research, knowledge gaps, opportunities, barriers, and approaches for prevention research for AsA and NHPI populations. The workshop covered 5 domains: 1) sociocultural, environmental, psychological health, and lifestyle dimensions; 2) metabolic disorders; 3) cardiovascular and lung diseases; 4) cancer; and 5) cognitive function and healthy aging. Two recurring themes emerged: Very limited data on the epidemiology, risk factors, and outcomes for most conditions are available, and most existing data are not disaggregated by subgroup, masking variation in risk factors, disease occurrence, and trajectories. Leveraging the vast phenotypic differences among AsA and NHPI groups was identified as a key opportunity to yield novel clues into etiologic and prognostic factors to inform prevention efforts and intervention strategies. Promising approaches for future research include developing collaborations with community partners, investing in infrastructure support for cohort studies, enhancing existing data sources to enable data disaggregation, and incorporating novel technology for objective measurement. Research on AsA and NHPI subgroups is urgently needed to eliminate disparities and promote health equity in these populations.

BackgroundAs global populations age, cross-national comparisons of cognitive health and dementia risk are increasingly valuable. It remains unclear, however, whether country-level differences in cognitive function are attributable to population differences or bias due to incommensurate measurement. To demonstrate an effective method for cross-national comparison studies, we aimed to statistically harmonize measures of episodic memory and language function across two population-based cohorts of older adults in the United States (HRS HCAP) and India (LASI-DAD).MethodsData for 3,496 HRS HCAP (≥65 years) and 3,152 LASI-DAD (≥60 years) participants were statistically harmonized for episodic memory and language performance using confirmatory factor analysis (CFA) methods. Episodic memory and language factor variables were investigated for differential item functioning (DIF) and precision.ResultsCFA models estimating episodic memory and language domains based on a priori adjudication of comparable items fit the data well. DIF analyses revealed that four out of ten episodic memory items and five out of twelve language items measured the underlying construct comparably across samples. DIF-modified episodic memory and language factor scores showed comparable patterns of precision across the range of the latent trait for each sample.ConclusionsHarmonization of cognitive measures will facilitate future investigation of cross-national differences in cognitive performance and differential effects of risk factors, policies, and treatments, reducing study-level measurement and administrative influences. As international aging studies become more widely available, advanced statistical methods such as those described in this study will become increasingly central to making universal generalizations and drawing valid conclusions about cognitive aging of the global population.

BACKGROUND: We investigated the effects of apolipoprotein E (APOE) ε4 and its interactions with sociodemographic characteristics on cognitive measures in South Asians from the Diagnostic Assessment of Dementia for the Longitudinal Aging Study of India (LASI‐DAD). METHODS: Linear regression was used to assess the association between APOE ε4 and global‐ and domain‐specific cognitive function in 2563 participants (mean age 69.6 ± 7.3 years; 53% female). Effect modification by age, sex, and education were explored using interaction terms and subgroup analyses. RESULTS: APOE ε4 was inversely associated with most cognitive measures (p < 0.05). This association was stronger with advancing age for the Hindi Mental State Examination (HMSE) score (βε4×age = −0.44, p = 0.03), orientation (βε4×age = −0.07, p = 0.01), and language/fluency (βε4×age = −0.07, p = 0.01), as well as in females for memory (βε4×male = 0.17, p = 0.02) and language/fluency (βε4×male = 0.12, p = 0.03). DISCUSSION: APOE ε4 is associated with lower cognitive function in South Asians from India, with a more pronounced impact observed in females and older individuals. Highlights: APOE ε4 carriers had lower global and domain‐specific cognitive performance.Females and older individuals may be more susceptible to ε4 effects.For most cognitive measures, there was no interaction between ε4 and education. [ABSTRACT FROM AUTHOR]

ObjectivesCardiovascular disease has become a major public health challenge in developing countries. The goal of this study is to compare socioeconomic status (SES) gradients of cardiovascular risk factors (CVRF) both within and between China and India.MethodsWe used multivariable logistic regression models to examine the associations between SES and CVRF, using data from the China health and retirement longitudinal study and the longitudinal aging study in India.ResultsThe results showed that, compared to illiteracy, the odds ratios of completing junior high school for high-risk waist circumference were 4.99 (95% confidence interval: 1.77-14.06) among Indian men, 3.42 (95% confidence interval: 1.66-7.05) among Indian women, but 0.74 (95% confidence interval: 0.59-0.92) among Chinese women. Similar patterns were observed between educational attainment and high-risk body mass index, and between education and hypertension, based on self-reported physician diagnosis and direct blood pressure measurements.ConclusionsSES is associated with CVRF in both China and India. However, this relationship showed opposite patterns across two countries, suggesting that this association is not fixed, but is subjective to underlying causal pathways, such as patterns of risky health behaviors and different social and health policies.

The Harmonized Cognitive Assessment Protocol (HCAP) is a major innovation that provides, for the first time, harmonized data for cross‐national comparisons of later‐life cognitive functions that are sensitive to linguistic, cultural, and educational differences across countries. However, cognitive function does not lend itself to direct comparison across diverse populations without careful consideration of the best practices for such comparisons. This perspective discusses theoretical and methodological considerations and offers a set of recommended best practices for conducting cross‐national comparisons of risk factor associations using HCAP data. Because existing and planned HCAP studies provide cognition data representing an estimated 75% of the global population ≥65 years of age, these recommended best practices will support high‐quality comparative analyses of cognitive aging around the world. The principles described in this perspective are applicable to any researcher aiming to integrate or compare harmonized data on cognitive outcomes and their risk and protective factors across diverse populations. [ABSTRACT FROM AUTHOR]

INTRODUCTION: We conducted a cross‐national comparison of the association between main lifetime occupational skills and later‐life cognitive function across four economically and socially distinct countries. METHODS: Data were from population‐based studies of aging and their Harmonized Cognitive Assessment Protocols (HCAPs) in the US, South Africa, India, and Mexico (N = 10,037; Age range: 50 to 105 years; 2016 to 2020). Main lifetime occupational skill was classified according to the International Standard Classification of Occupations. Weighted, adjusted regression models estimated pooled and country‐specific associations between main lifetime occupational skill and later‐life general cognitive function in men and women. RESULTS: We observed positive gradients between occupational skill and later‐life cognitive function for men and women in the US and Mexico, a positive gradient for women but not men in India, and no association for men or women in South Africa. DISCUSSION: Main lifetime occupations may be a source of later‐life cognitive reserve, with cross‐national heterogeneity in this association. Highlights: No studies have examined cross‐national differences in the association of occupational skill with cognition.We used data from Harmonized Cognitive Assessment Protocols in the US, Mexico, India, and South Africa.The association of occupational skill with cognitive function varies by country and gender. [ABSTRACT FROM AUTHOR]

Background: Low and middle-income countries like India anticipate rapid population aging and increases in dementia burden. In India, dementia screening scales originally developed in other contexts need to be assessed for feasibility and validity, given the number of different languages and varying levels of literacy and education. Method: Using data from the Longitudinal Aging Study in India-Diagnostic Assessment of Dementia (N = 4,028), we characterize the performance of the Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE). We described patterns and correlates of missingness, evaluated the psychometric properties of the scale, and assessed criterion validity against the Hindi Mental State Examination (HMSE) using linear regression. Results: Several IQCODE items had high levels of missingness, which was associated with urbanicity, respondent's gender, and informant's generation (same vs. younger generation). Full IQCODE scores showed strong criterion validity against the HMSE; each 1-point increase in IQCODE score was associated with a 3.03-point lower score on the HMSE, controlling for age, gender, and urbanicity. The statistically significant association between IQCODE and HMSE was stronger in urban than rural settings (p-value for interaction = 0.04). Associations between IQCODE and HMSE remained unchanged after removing the three items with the highest levels of differential missingness (remembering addresses and telephone numbers, ability to work with familiar machines, ability to learn to use new gadget or machine). Conclusion: Findings raise questions about the value of including items with high proportions of missingness, which may signal cultural irrelevance, while removing them did not affect criterion validity. [ABSTRACT FROM AUTHOR]