Community-level Data Research Articles

A Mixed Effect Similarity Matrix Regression Model (SMRmix) for Integrating Multiple Microbiome Datasets at Community Level.

Recent studies have highlighted the importance of human microbiota in our health and diseases. However, in many areas of research, individual microbiome studies often offer inconsistent results due to the limited sample sizes and the heterogeneity in study populations and experimental procedures. This inconsistency underscores the necessity for integrative analysis of multiple microbiome datasets. Despite the critical need, statistical methods that incorporate multiple microbiome datasets and account for the study heterogeneity are not available in the literature. In this paper, we develop a mixed effect similarity matrix regression (SMRmix) approach for identifying community level microbiome shifts between outcomes. SMRmix has a close connection with the microbiome kernel association test, one of the most popular approaches for such a task but is only applicable when we have a single study. SMRmix enables researchers to consolidate findings from diverse microbiome studies. Via extensive simulations, we show that SMRmix has well-controlled type I error and higher power than some potential competitors. We applied the SMRmix to two real-world datasets. The first, from the HIV-reanalysis consortium, integrated data from 17 studies on gut dysbiosis in HIV. Our analysis confirmed consistent associations between the gut microbiome and HIV infection as well as MSM (men who have sex with men) status, demonstrating greater power than competing methods. The second dataset involved 11 studies on the gut microbiome in colorectal cancer; analysis with SMRmix confirmed significant dysbiosis in affected individuals compared to healthy controls. The development of SMRmix enables the integration of multiple studies and effectively managing study heterogeneity, and provides a powerful tool for uncovering consistent associations between diseases and community-level microbiome data.

Opioid Abatement Toolkit: Using Community-Level Data to Address the Spectrum of Needs and Priorities across Virginia’s Cities

Opioid Abatement Toolkit: Using Community-Level Data to Address the Spectrum of Needs and Priorities across Virginia’s Cities

Abstract Su908: Unfavorable prognostic features and temporal trends in Out-of-Hospital Cardiac Arrests (OHCA) presenting to an Academic Center at the Border in El Paso

Introduction: Early recognition of cardiac arrest with bystander cardiopulmonary resuscitation (CPR), and early defibrillation when appropriate, can improve outcomes after Out of Hospital Cardiac Arrest (OHCA). Data from the national registries indicate there are both regional as well racial and ethnic differences in rates of bystander CPR in witnessed OHCA cases with significantly lower rates of bystander CPR in non-white persons. However, Hispanic as well border populations are underrepresented in these studies. University Medical Center (UMC) El Paso, Texas is an academic medical center located 1 mile from the US- Mexico border which is also the only not for profit safety net hospital with a Level1 trauma center. Research Question: Outcomes after OHCA are strongly affected by peri-arrest circumstances including bystander CPR and certain features portend poor outcomes. We performed a descriptive observational study of OHCA cases presenting to UMC to analyze temporal trends in these factors in our community as local data from El Paso is currently not reported in any registries. Methods: We collaborated with the El Paso Fire Department (EPFD) to obtain resuscitation data on patients who suffered OHCA and were brought to the University Medical Center (UMC)Emergency Department (ED) and created a registry of patients who suffered OHCA in El Paso FROM 2017 to Nov 2021. Results: Initial analysis identified around 698 predominantly Hispanic patients who suffered OHCA. Of these 115 were due to non-cardiac causes such as trauma, and 583 were possible cardiac etiology. OHCA cases peaked in 2020 coinciding with COVID 19 surge. Less than one third (31.5%) had bystander CPR with only 11.9% having a shockable rhythm. 64% did not achieve Return of spontaneous circulation in the field. 39% expired in the ED or in the field. We also performed a temporal analysis of these features and bystander CPR rates peaked in 2019 (38.1%to 24.8% in 2021). Conclusion: In our Hispanic underserved population in El Paso, we identified poor outcomes after OHCA, with non-shockable rhythm being the most common adverse feature and decreased rates of bystander CPR (31.5%) with further decrease after 2019. Local community level data which is critical for appropriate local resource allocation to improve OHCA outcomes is missing from underserved populations and additional resources are required improve outcomes.

The local dynamics of sociostructural features and HIV stigma in the HPTN 071 (PopART) trial: An analysis of community-level data from Zambia and South Africa.

The local dynamics of sociostructural features and HIV stigma in the HPTN 071 (PopART) trial: An analysis of community-level data from Zambia and South Africa.

Intersecting social and environmental determinants of multidrug-resistant urinary tract infections in East Africa beyond antibiotic use

The global health crisis of antibacterial resistance (ABR) poses a particular threat in low-resource settings like East Africa. Interventions for ABR typically target antibiotic use, overlooking the wider set of factors which drive vulnerability and behaviours. In this cross-sectional study, we investigated the joint contribution of behavioural, environmental, socioeconomic, and demographic factors associated with higher risk of multi-drug resistant urinary tract infections (MDR UTIs) in Kenya, Tanzania, and Uganda. We sampled outpatients with UTI symptoms in healthcare facilities and linked their microbiology data with patient, household and community level data. Using bivariate statistics and Bayesian profile regression on a sample of 1610 individuals, we show that individuals with higher risk of MDR UTIs were more likely to have compound and interrelated social and environmental disadvantages: they were on average older, with lower education, had more chronic illness, lived in resource-deprived households, more likely to have contact with animals, and human or animal waste. This suggests that interventions to tackle ABR need to take account of intersectional socio-environmental disadvantage as a priority.

Enhancement of a social risk score in the electronic health record to identify social needs among medically underserved patients: using structured data and free-text provider notes.

To improve the performance of a social risk score (a predictive risk model) using electronic health record (EHR) structured and unstructured data. We used EPIC-based EHR data from July 2016 to June 2021 and linked it to community-level data from the US Census American Community Survey. We identified predictors of interest within the EHR structured data and applied natural language processing (NLP) techniques to identify patients' social needs in the EHR unstructured data. We performed logistic regression models with and without information from the unstructured data (Models I and II) and compared their performance with generalized estimating equation (GEE) models with and without the unstructured data (Models III and IV). The logistic model (Model I) performed well (Area Under the Curve [AUC] 0.703, 95% confidence interval [CI] 0.701:0.705) and the addition of EHR unstructured data (Model II) resulted in a slight change in the AUC (0.701, 95% CI 0.699:0.703). In the logistic models, the addition of EHR unstructured data resulted in an increase in the area under the precision-recall curve (PRC 0.255, 95% CI 0.254:0.256 in Model I versus 0.378, 95% CI 0.375:0.38 in Model II). The GEE models performed similarly to the logistic models and the addition of EHR unstructured data resulted in a slight change in the AUC (0.702, 95% CI 0.699:0.705 in Model III versus 0.699, 95% CI 0.698:0.702 in Model IV). Our work presents the enhancement of a novel social risk score that integrates community-level data with patient-level data to systematically identify patients at increased risk of having future social needs for in-depth assessment of their social needs and potential referral to community-based organizations to address these needs. The addition of information on social needs extracted from unstructured EHR resulted in an improved prediction of positive cases presented by the improvement in the PRC.

Microbiome–metabolite linkages drive greenhouse gas dynamics over a permafrost thaw gradient

Interactions between microbiomes and metabolites play crucial roles in the environment, yet how these interactions drive greenhouse gas emissions during ecosystem changes remains unclear. Here we analysed microbial and metabolite composition across a permafrost thaw gradient in Stordalen Mire, Sweden, using paired genome-resolved metagenomics and high-resolution Fourier transform ion cyclotron resonance mass spectrometry guided by principles from community assembly theory to test whether microorganisms and metabolites show concordant responses to changing drivers. Our analysis revealed divergence between the inferred microbial versus metabolite assembly processes, suggesting distinct responses to the same selective pressures. This contradicts common assumptions in trait-based microbial models and highlights the limitations of measuring microbial community-level data alone. Furthermore, feature-scale analysis revealed connections between microbial taxa, metabolites and observed CO2 and CH4 porewater variations. Our study showcases insights gained by using feature-level data and microorganism–metabolite interactions to better understand metabolic processes that drive greenhouse gas emissions during ecosystem changes.

Relationships between crayfish population genetic diversity, species richness, and abundance within impounded and unimpounded streams in Alabama, USA.

Understanding the relationship between multi-scale processes driving community- and population-level diversity can guide conservation efforts. While the importance of population-level genetic diversity is widely recognized, it is not always assessed for conservation planning, and positive correlations with community-level diversity are sometimes assumed, such that only the latter is measured. We surveyed species richness and cumulative multispecies abundance of crayfishes in impounded and unimpounded streams in the southern Appalachian Mountains (Alabama, USA). We simultaneously assessed levels of population genetic diversity within two focal crayfishes (Faxonius validus and F. erichsonianus) using nuclear (nDNA; inter-simple sequence repeat (ISSR)) and mitochondrial DNA (mtDNA; mitochondrial DNA cytochrome oxidase subunit I (mtCOI)) markers. We then tested for species-genetic diversity correlations (SGDCs), species diversity-abundance correlations (i.e., more individuals hypothesis, MIH), and abundance-genetic diversity correlations (AGDCs) across sites. We also examined the relationship between each of the three different types of correlation (i.e., species richness, cumulative multispecies abundance, and population genetic diversity) and stream habitat characteristics and fragmentation. Surprisingly, based on F. validus mtDNA data, sites with the greatest multispecies abundance had the lowest genetic diversity, indicating a negative AGDC. However, no AGDC was evident from nDNA. There was no evidence of SGDCs for F. validus based on either of the two genetic data types. For F. erichsonianus, there was no evidence for SGDC or AGDC. When considering the community-level data only, there was no support for the MIH. Stream width was positively correlated with F. validus genetic diversity, but negatively correlated with multispecies abundance. Similarly, species richness was positively correlated with stream width in unimpounded streams but negatively correlated with width in impounded streams. These findings indicate that community-level diversity cannot be indiscriminately used as a proxy for population-level diversity without empirically testing this correlation on the focal group. As such, community- and population-level assessments for multiple crayfish species are needed to better understand drivers of diversity and eco-evolutionary processes which will aid in the conservation of this vulnerable taxonomic group.

Linkage of Hospital and Community level records for Epidemiological Research: Experiences from Kagando Health and Demographic Surveillance System in Western Uganda

BackgroundLinkage of census data and hospital records is crucial in conducting epidemiological research to inform public health decisions. In this study, we share our experiences in linking community-level data collected through census with hospital records and highlight the challenges and opportunities from our engagements with stakeholders. MethodologyWe undertook engagements with Uganda Christian University Ethics Research Committee, Hospital Management, and Community Health Volunteers to set up a platform to integrate hospital and census records using key identifiers. Trained data clerks prospectively linked data during patients’ visits following the data protection guidelines. ResultsFrom a population of approximately 30,000 people, we managed to link 1000 records within three months. Lessons learnt and opportunities: It is possible to link data with a set of identifiers, not only relying on common identifiers, especially for communities where ownership of a national identity card or birth certificate is not universal. Building stakeholder partnerships is essential, and frequent meetings with the hospital staff, epidemiologists and data programmers helped to identify problems and solutions. ChallengesLack of local capacity at the health facilities to develop linkage platforms. However, after development they can efficiently run the program. Frequent changes in the versions of the electronic system were later resolved. RecommendationsThere is a need to conduct regular capacity building for stakeholders to help them understand the importance of data linkage. ConclusionPublic engagement is an ethical imperative that enhances the quality, credibility, and long-term impact of research by incorporating the perspectives of the communities being studied.

Evaluating the Effects of Long-Term Exposure to Fine Particulate Matter (PM2.5) on Depressive Mood among Korean Older Adults Using Multilevel Analysis.

The harmful effects of particulate matter (PM) are amplified in older adults, who experience a decline in physiological function, reducing their ability to expel and detoxify inhaled PM. Moreover, older adults may be more vulnerable to fine particulate toxicity due to underlying medical conditions. We assessed the effects of long-term exposure to fine particulate matter (PM2.5) on depressive mood (DM) in adults aged > 65 yr using community-based data. In the 2017, Korean Community Health Survey (KCHS) data, data of PM2.5 and community factors were constructed based on participants who responded to DM in a sample of 67,802 individuals. To evaluate the effect of PM2.5 on DM among older adults, a multilevel regression model was constructed using individual-(KCHS) and community-level data (PM2.5, green area per capita, urban area, social welfare budget, health, and social business). For PM2.5, an independent variable, quartiles were used to classify regions according to concentration. A positive correlation was found between PM2.5 and DM of older adults. Compared to Q1, the odds ratio increased to 1.15 (95% CI 0.76-1.74) in Q2, 1.55 (95% CI 1.02-2.35) in Q3, and 1.60 (95% CI 1.16-2.20) in Q4. DM may increase in older individuals living in areas with high PM2.5. Systematic mental health management is required for older individuals residing in such areas.

Disparities in the initial presentation of melanoma across two socioeconomically diverse New York City neighborhoods.

1593 Background: Disparities in stage at diagnosis among melanoma patients are often seen between urban and rural communities, with patients in rural areas being diagnosed with more advanced tumors. Factors contributing to the disparities include decreased access to dermatologists in rural areas, and lower socioeconomic status (SES). We investigated urban disparities in melanoma T stage at diagnosis among patients residing in 2 New York City (NYC) neighborhoods of differing SES and receiving care within the NYU Langone Health System. The neighborhoods were: Upper East Side (UES) and Brighton Beach/Coney Island (BB/CI). Methods: We conducted a retrospective chart review (NYU IRB 23-01020) of melanoma patients (N=243) diagnosed from 2018-2022 using ICD-10-CM codes: C43 (malignant melanoma of skin); D03 (melanoma in situ); and Z85.820 (personal history of malignant melanoma of skin). For community-level data we used the American Academy of Dermatology’s “Find a Dermatologist” search function to locate member-dermatologists; New York State Cancer Registry data (2016-2020) to determine annual melanoma incidence; and the United States Census Bureau Public Use Microdata Areas to determine the proportion of Non-Hispanic Whites (NHW), income levels, and educational attainment. The distribution of T stages was compared using a chi-square test. A two-sample test was used to assess equality of proportions. Results: In UES, the annual melanoma incidence was 30.2/100,000 (95% CI: 27.4-33.2); NHW comprised 74.6% of the population; the median household income was $135,820; 78% attained education higher than high school; and there are 190 dermatologists within a 0.5-mile radius. In BB/CI the annual melanoma incidence was 14/100,000 (95% CI: 11.6-16.9); NHW comprised 55.1% of the population; the median household income was $43,118; 46% attained education higher than high school; and there is 1 dermatologist within a 0.5-mile radius. There are 15 dermatologists within a 3.0-mile radius. 155 and 88 patients met inclusion criteria in UES and BB/CI respectively. The distribution of T stages (i.e. Tis to T4) was significantly different between UES and BB/CI with higher proportions of advanced stage tumors in BB/CI (p=0.0002). Specifically, the proportion of (T2+T3+T4) tumors/total melanomas was 35/155 (23%) in UES; and 41/88(47%) in BB/CI (p<0.0001). For reference, the proportion of T2+T3+T4 melanomas in the United States is 30%. Conclusions: We identified substantial disparities in the initial presentation of melanoma in 2 NYC neighborhoods, with proportionately more advanced stage tumors in the community of low educational attainment, less access to dermatologic services, and lesser household income. Neighborhood-based approaches to uncover melanoma disparities can identify areas for community outreach and engagement efforts to improve melanoma awareness and access to dermatologic care.

Exploring the impacts of individual residential mobility, housing, and social disorganization on recidivism among parolees

The impact of housing and individual residential mobility on recidivism is nuanced. Individuals may move from prosocial environments to criminogenic environments, or from neighborhoods that are more, or less, socioeconomically disadvantaged. We explore these phenomena using data on individuals on parole in the District of Columbia with community-level Census data. We hypothesize that residential mobility will affect recidivism through changes in both housing types and neighborhood characteristics. Findings suggest that people immediately placed into treatment-oriented or transitional housing had lower rates of rearrest than those in other housing situations. Results of the community-level measures of social disorganization were mixed.

GWmodelS: a standalone software to train geographically weighted models

ABSTRACT With the recent increase in studies on spatial heterogeneity, geographically weighted (GW) models have become an essential set of local techniques, attracting a wide range of users from different domains. In this study, we demonstrate a newly developed standalone GW software, GWmodelS using a community-level house price data set for Wuhan, China. In detail, a number of fundamental GW models are illustrated, including GW descriptive statistics, basic and multiscale GW regression, and GW principle component analysis. Additionally, functionality in spatial data management and batch mapping are presented as essential supplementary activities for GW modeling. The software provides significant advantages in terms of a user-friendly graphical user interface, operational efficiency, and accessibility, which facilitate its usage for users from a wide range of domains.

Effectiveness of Using AI-Driven Hotspot Mapping for Active Case Finding of Tuberculosis in Southwestern Nigeria.

Background: Nigeria is among the top five countries that have the highest gap between people reported as diagnosed and estimated to have developed tuberculosis (TB). To bridge this gap, there is a need for innovative approaches to identify geographical areas at high risk of TB transmission and targeted active case finding (ACF) interventions. Leveraging community-level data together with granular sociodemographic contextual information can unmask local hotspots that could be otherwise missed. This work evaluated whether this approach helps to reach communities with higher numbers of undiagnosed TB. Methodology: A retrospective analysis of the data generated from an ACF intervention program in four southwestern states in Nigeria was conducted. Wards (the smallest administrative level in Nigeria) were further subdivided into smaller population clusters. ACF sites and their respective TB screening outputs were mapped to these population clusters. This data were then combined with open-source high-resolution contextual data to train a Bayesian inference model. The model predicted TB positivity rates on the community level (population cluster level), and these were visualised on a customised geoportal for use by the local teams to identify communities at high risk of TB transmission and plan ACF interventions. The TB positivity yield (proportion) observed at model-predicted hotspots was compared with the yield obtained at other sites identified based on aggregated notification data. Results: The yield in population clusters that were predicted to have high TB positivity rates by the model was at least 1.75 times higher (p-value < 0.001) than the yield in other locations in all four states. Conclusions: The community-level Bayesian predictive model has the potential to guide ACF implementers to high-TB-positivity areas for finding undiagnosed TB in the communities, thus improving the efficiency of interventions.

Integrating a Community-Based Health Information System with a Patient-Centered Medical Home to improve care of patients with hypertension: a longitudinal observational study protocol

BackgroundThe primary healthcare system in Pakistan focuses on providing episodic, disease-based care. Health care for low-middle income communities is largely through a fee-for-service model that ignores preventive and health-promotive services. The growing burden of cardiovascular illnesses requires restructuring of the primary health care system allowing a community-to-clinic model of care to improve patient- and community-level health indicators.MethodsWe propose a model that integrates a Patient-Centered Medical Home (PCMH) with a Community-Based Health Information System (CBHIS) using hypertension (HTN) as an example. This protocol describes the integration and evaluation of the PCMH-CBHIS infrastructure through a population-based, observational, longitudinal study in a low-middle income, urban community in Pakistan. Participants are being enrolled in CBHIS and will be followed longitudinally over two years for HTN outcomes. A mixed-methods approach is adopted to evaluate the process of integrating PCMH with CBHIS. This involves building partnerships with the community through formal and informal meetings, focus group discussions, and a household health assessment survey (HAS). Community members identified with HTN are linked to PCMH for disease management. A customized electronic medical record system links community-level data with patient-level data to track changes in disease burden. The RE-AIM evaluation framework will be used to monitor community and individual-level metrics to guide implementation assessment, the potential for generalization, and the effectiveness of the PCMH in improving HTN-related health outcomes. Ethical clearance has been obtained from the Ethics Review Committee at Aga Khan University (2022-6723-20985).DiscussionThis study will evaluate the value of restructuring the primary care health system by ensuring systematic community engagement and measurement of health indicators at the patient- and community-level. While HTN is being used as a prototype to generate evidence for the effectiveness of this model, findings from this initiative will be leveraged towards strengthening the management of other acute and chronic conditions in primary care settings. If effective, the model can be used in Pakistan and other LMICs and resource-limited settings.

How Can Community Data Be Leveraged to Advance Primary Health Care? A Scoping Review of Community-Based Health Information Systems.

Community-based health information systems (CBISs) can provide critical insights into how community health systems function, and digitized CBISs may improve the quality of community-level data and facilitate integration and use of CBISs within the broader health system. This scoping review aims to understand how CBISs have been implemented, integrated, and used to support community health outcomes in low- and middle-income countries (LMICs). Both peer-reviewed and gray literature were included; relevant articles were identified using key terms and controlled vocabulary related to community/primary health care, health information systems, digital health, and LMICs. A total of 11,611 total records were identified from 5 databases and the gray literature. After deduplication, 6,985 peer-reviewed/gray literature were screened, and 95 articles/reports were included, reporting on 105 CBIS implementations across 38 countries. Findings show that 55% of CBISs included some level of digitization, with just 28% being fully digitized (for data collection and reporting). Data flow from the community level into the health system varied, with digitized CBISs more likely to reach national-level integration. National-level integration was primarily seen among vertical CBISs. Data quality challenges were present in both paper-based and digitized CBISs, exacerbated by fragmentation of the community health landscape with often parallel reporting systems. CBIS data use was constrained to mostly vertical and digitized (partially or fully) CBISs at national/subnational levels. Digitization can play a pivotal role in strengthening CBIS use, but findings demonstrate that CBISs are only as effective as the community health systems they are embedded within. Community-level data are often not being integrated into national/subnational health information systems, undermining the ability to understand what the community health needs are. Furthermore, stronger investments within community health systems need to be in place broadly to reduce fragmentation and provide stronger infrastructural and systemic support to the community health workforce.

Individual- and Community-Level Predictors of Hospital-at-Home Outcomes.

Advanced Care at Home is a Mayo Clinic hospital-at-home (HaH) program that provides hospital-level care for patients. The study examines patient- and community-level factors that influence health outcomes. The authors performed a retrospective study using patient data from July 2020 to December 2022. The study includes 3 Mayo Clinic centers and community-level data from the Agency for Healthcare Research and Quality. The authors conducted binary logistic regression analyses to examine the relationship among the independent variables (patient- and community-level characteristics) and dependent variables (30-day readmission, mortality, and escalation of care back to the brick-and-mortar hospital). The study examined 1433 patients; 53% were men, 90.58% were White, and 68.2% were married. The mortality rate was 2.8%, 30-day readmission was 11.4%, and escalation back to brick-and-mortar hospitals was 8.7%. At the patient level, older age and male gender were significant predictors of 30-day mortality (P-value <0.05), older age was a significant predictor of 30-day readmission (P-value <0.05), and severity of illness was a significant predictor for readmission, mortality, and escalation back to the brick-and-mortar hospital (P-value <0.01). Patients with COVID-19 were less likely to experience readmission, mortality, or escalations (P-value <0.05). At the community level, the Gini Index and internet access were significant predictors of mortality (P-value <0.05). Race and ethnicity did not significantly predict adverse outcomes (P-value >0.05). This study showed promise in equitable treatment of diverse patient populations. The authors discuss and address health equity issues to approximate the vision of inclusive HaH delivery.

Abstract P198: Developing a Practical, Evidence-Based Framework for Evaluating Community Nutrition Security Interventions

Introduction: Addressing nutrition insecurity at a population level is complex and requires changing policies, systems, and environments. The American Heart Association (AHA) has identified 17 research informed systemic interventions that have the potential to increase nutrition security over time. These interventions can be leveraged individually or together based on community needs and assets. Evaluating these interventions is essential for understanding impact, improving implementation, and allocating resources to effective strategies. This project sought to establish a pragmatic yet rigorous framework for evaluating the health impact of these strategies in communities across the United States. Hypothesis: We assessed the hypothesis that we could evaluate community nutrition security interventions by using program records, secondary data sources, and judicious use of primary data collection. Methods: Researchers reviewed details from the AHA's community impact database detailing 188 research informed nutrition security projects in communities across 29 states and the District of Columbia. Impacts were assessed for reach of intervention and strength of evidence to determine relative population dose of interventions. Seven interventions were determined to be ‘high dose,’ and researchers identified community and proximal outcomes for three high-dose interventions where the AHA is typically the catalyst that is leading and influencing work with community partners. Researchers reviewed 11 national databases to identify existing community-level data aggregated at the county, city, zip code, or census track that may be associated with the three high dose impact opportunities. Researchers then reviewed the evidence-based literature associated with proximal outcomes - organizational, environmental, policy, or individual/household changes - that have been documented in research studies in association with the three high dose interventions. Using the insight from both reviews, researchers were able to develop a model evaluation framework for assessing the impact of community level interventions over time. Results: Researchers were able to identify 17 proximal outcomes and two community level outcomes with evidence associated with the three high dose interventions. Conclusion: Demonstrating the impact of community level nutrition security interventions is challenging given the complexity of the environments in which they are implemented and the limitations of evaluation resources. This approach prioritizes high dose evidence-based interventions and then leverages existing community level datasets and organizational measures to assess impact. It provides a framework for other organizations to prioritize specific systemic interventions to evaluate and further informs the evidence base for future interventions.

Liquidity constraints and speculation: Evidence from the housing-resale restriction policy in Qingdao, China

ABSTRACT This article seeks to explore the efficacy of the Housing-Resale Restriction (HRR) policy in curtailing housing speculation, complementing existing literature that primarily examines the impact of taxation. The HRR policy directly reduces speculative opportunities through heightened liquidity constraints. Employing the methodology of regression discontinuity in time (RDiT), this study analyzes the impact of liquidity constraints on housing price growth rates based on the monthly community-level data from Qingdao, China, where the HRR policy was the most stringent in terms of scope and holding time requirement. The results show that the HRR policy reduced housing price growth rates by 1.3881 to 2.1001% points on average. Moreover, the disincentive impact was less pronounced in affluent, recently developed communities and those near subway stations. These findings provide valuable insights for policymakers to regulate the real estate market, mitigate excessive speculation, and prevent market bubbles.

Understanding the need for pediatric neurodevelopmental services among households in western Kenya: A population-based study.

Existing estimates of rates of childhood disability in Kenya are based on data with important limitations. Individual-level data on childhood disability at the local level is also lacking, leaving critical knowledge gaps for clinical and programmatic development. We aimed to estimate the rates of children at-risk for disability, examine the external factors related to risk of childhood disability, and gain a better understanding of the challenges experienced by children at-risk for disabilities and their families within western Kenya. We conducted a small, cross-sectional randomized community survey to assess the rates of childhood disability across six administrative locations in Uasin Gishu County, Kenya, and to understand the experiences of these children and their caretakers. Rate of childhood disability in Uasin Gishu county was estimated to be 5%, with the most common disabilities being mood disorders. Caretakers reported several barriers to accessing treatment for their children at-risk of having disabilities, including financial concerns and lack of transportation. Our findings suggest a need for improved access to care in this region, including addressing significant barriers to accessing care such as stigma and socioeconomic challenges. These community-level data will inform the development of future infrastructure and programming for this population.