Use of computer simulation to identify effects on hospital census with reduction of transfers for non-procedural patients in community hospitals.

OBJECTIVE: In-person healthcare delivery is rapidly changing with a shifting employment landscape and technological advances. Opportunities to care for patients in more efficient ways include leveraging technology and focusing on caring for patients in the right place at the right time. We aim to use computer modelling to understand the impact of interventions, such as virtual consultation, on hospital census for referring and referral centres if non-procedural patients are cared for locally rather than transferred. PATIENTS AND METHODS: We created computer modelling based on 25 138 hospital transfers between June 2019 and June 2022 with patients originating at one of 17 community-based hospitals and a regional or academic referral centre receiving them. We identified patients that likely could have been cared for at a community facility, with attention to hospital internal medicine and cardiology patients. The model was run for 33 500 days. RESULTS: Approximately 121 beds/day were occupied by transferred patients at the academic centre, and on average, approximately 17 beds/day were used for hospital internal medicine and nine beds/day for non-procedural cardiology patients. Typical census for all internal medicine beds is approximately 175 and for cardiology is approximately 70. CONCLUSION: Deferring transfers for patients in favour of local hospitalisation would increase the availability of beds for complex care at the referral centre. Potential downstream effects also include increased patient satisfaction due to proximity to home and viability of the local hospital system/economy, and decreased resource utilisation for transfer systems.

Assessing the heterogeneity of ethnic categories for patient records in primary care.

BACKGROUND: UK general practice surgeries collect data regarding patient ethnicities, typically at registration. These data are subsequently used in both clinical care and research, for example, when embedded in risk modelling tools. The published standard list of ethnic categories exists, but little is known about what happens in frontline practice. AIM: To document the variation in ethnic categories available on online patient registration forms across GP surgeries in Oxfordshire. METHOD: Of all 67 GP surgeries in Oxfordshire, 56 had online registration forms that included an option list for ethnicity reporting. The authors compared these against the 2001, 2011, and 2021 UK census ethnic group categorisation. RESULTS: Significant heterogeneity was identified across practices. The number of options for ethnicity group ranged from 5 to 84, with a median of 14, compared to the census lists that comprise of 19 (2021), 18 (2011), and 16 (2001) groups. Of the 56 practices, six used the 2001 census list, five used the 2011 census list, and none used the 2021 census list. Overall, 45 practices used lists that differed from any census list, including categories not typically considered to be ethnic, for example 'Muslim' or 'Buddhist', meaning individuals could potentially identify with multiple options. CONCLUSION: High-quality research and healthcare data that includes patient ethnicity is essential to understand, document, and mitigate against health inequalities. However, this may be compromised by poorly conceived ethnic categorisations and a lack of standardisation. This pilot/exploratory study suggests that the ethnicity records in primary health care may be neither standardised nor meaningful.

Long-term health conditions among household families in Aotearoa New Zealand: cross-sectional analysis of integrated Census and administrative data.

AIM: Little is known about the extent to which families in Aotearoa New Zealand are affected by long-term health conditions (HCs). This study aimed to explore the rates of nine selected HCs among New Zealand family members within the same household. METHOD: Linked population and administrative health data were obtained for families living in the same household according to the 2013 New Zealand Census (N=1,043,172). Health data (2008-2013) were used to ascertain whether people in these families (N=3,137,517) received treatment or services for nine selected HCs: cancer, chronic obstructive pulmonary disease, heart disease, diabetes, dementia, gout, stroke, traumatic brain injury (TBI), or mental health/behaviour conditions (MHBCs). RESULTS: Over 60% of families included at least one person with a HC, and this rate was higher among multi-generation families (73.9%). The most common HCs were MHBCs (39.4% of families), diabetes (16.0%) and TBI (13.9%). At the highest level of socio-economic deprivation, 57.6% of children aged under 18 years lived with a family member who had a HC. CONCLUSION: Three in five New Zealand household families included someone with at least one of nine selected HCs, with differences in the proportion affected according to family composition, socio-economic status and an individual's ethnicity. This suggests that there are a substantial number of people at risk of the poor outcomes associated with the experience of HCs within their family.

Mobility census for monitoring rapid urban development.

Monitoring urban structure and development requires high-quality data at high spatio-temporal resolution. While traditional censuses have provided foundational insights into demographic and socio-economic aspects of urban life, their pace may not always align with the pace of urban development. To complement these traditional methods, we explore the potential of analysing alternative big-data sources, such as human mobility data. However, these often noisy and unstructured big data pose new challenges. Here, we propose a method to extract meaningful explanatory variables and classifications from such data. Using movement data from Beijing, which are produced as a by-product of mobile communication, we show that meaningful features can be extracted, revealing, for example, the emergence and absorption of subcentres. This method allows the analysis of urban dynamics at a high-spatial resolution (here 500 m) and near real-time frequency, and high computational efficiency, which is especially suitable for tracing event-driven mobility changes and their impact on urban structures.

Reweighting UK Biobank corrects for pervasive selection bias due to volunteering.

BACKGROUND: Biobanks typically rely on volunteer-based sampling. This results in large samples (power) at the cost of representativeness (bias). The problem of volunteer bias is debated. Here, we (i) show that volunteering biases associations in UK Biobank (UKB) and (ii) estimate inverse probability (IP) weights that correct for volunteer bias in UKB. METHODS: Drawing on UK Census data, we constructed a subsample representative of UKB's target population, which consists of all individuals invited to participate. Based on demographic variables shared between the UK Census and UKB, we estimated IP weights (IPWs) for each UKB participant. We compared 21 weighted and unweighted bivariate associations between these demographic variables to assess volunteer bias. RESULTS: Volunteer bias in all associations, as naively estimated in UKB, was substantial-in some cases so severe that unweighted estimates had the opposite sign of the association in the target population. For example, older individuals in UKB reported being in better health, in contrast to evidence from the UK Census. Using IPWs in weighted regressions reduced 87% of volunteer bias on average. Volunteer-based sampling reduced the effective sample size of UKB substantially, to 32% of its original size. CONCLUSIONS: Estimates from large-scale biobanks may be misleading due to volunteer bias. We recommend IP weighting to correct for such bias. To aid in the construction of the next generation of biobanks, we provide suggestions on how to best ensure representativeness in a volunteer-based design. For UKB, IPWs have been made available.

Exploring COVID-19 census burdens by US hospital characteristics: Implications of quality reporting at rural and critical access hospitals.

PURPOSE: By assessing longitudinal associations between COVID-19 census burdens and hospital characteristics, such as bed size and critical access status, we can explore whether pandemic-era hospital quality benchmarking requires risk-adjustment or stratification for hospital-level characteristics. METHODS: We used hospital-level data from the US Department of Health and Human Services including weekly total hospital and COVID-19 censuses from August 2020 to August 2023 and the 2021 American Hospital Association survey. We calculated weekly percentages of total adult hospital beds containing COVID-19 patients. We then calculated the number of weeks each hospital spent at Extreme (≥20% of beds occupied by COVID-19 patients), High (10%-19%), Moderate (5%-9%), and Low (<5%) COVID-19 stress. We assessed longitudinal hospital-level COVID-19 stress, stratified by 15 hospital characteristics including joint commission accreditation, bed size, teaching status, critical access hospital status, and core-based statistical area (CBSA) rurality. FINDINGS: Among n = 2582 US hospitals, the median(IQR) weekly percentage of hospital capacity occupied by COVID-19 patients was 6.7%(3.6%-13.0%). 80,268/213,383 (38%) hospital-weeks experienced Low COVID-19 census stress, 28% Moderate stress, 22% High stress, and 12% Extreme stress. COVID-19 census burdens were similar across most hospital characteristics, but were significantly greater for critical access hospitals. CONCLUSIONS: US hospitals experienced similar COVID-19 census burdens across multiple institutional characteristics. Evidence-based inclusion of pandemic-era outcomes in hospital quality reporting may not require significant hospital-level risk-adjustment or stratification, with the exception of rural or critical access hospitals, which experienced differentially greater COVID-19 census burdens and may merit hospital-level risk-adjustment considerations.

Evaluating bias and noise induced by the U.S. Census Bureau's privacy protection methods.

The U.S. Census Bureau faces a difficult trade-off between the accuracy of Census statistics and the protection of individual information. We conduct an independent evaluation of bias and noise induced by the Bureau's two main disclosure avoidance systems: the TopDown algorithm used for the 2020 Census and the swapping algorithm implemented for the three previous Censuses. Our evaluation leverages the Noisy Measurement File (NMF) as well as two independent runs of the TopDown algorithm applied to the 2010 decennial Census. We find that the NMF contains too much noise to be directly useful without measurement error modeling, especially for Hispanic and multiracial populations. TopDown's postprocessing reduces the NMF noise and produces data whose accuracy is similar to that of swapping. While the estimated errors for both TopDown and swapping algorithms are generally no greater than other sources of Census error, they can be relatively substantial for geographies with small total populations.

Application of citizen science with the nationwide bird census.

Citizen science (CS) is the most effective tool for overcoming the limitations of government and/or professional data collection. To compensate for quantitative limitations of the 'Winter Waterbird Census of Korea', we conducted a total of four bird monitoring via CS from 2021 to 2022. To use CS data alongside national data, we studied CS data quality and improvement utilizing (1) digit-based analysis using Benford's law and (2) comparative analysis with national data. In addition, we performed bird community analysis using CS-specific data, demonstrating the necessity of CS. Neither CS nor the national data adhered to Benford's law. Alpha diversity (number of species and Shannon index) was lower, and total beta diversity was higher for the CS data than national data. Regarding the observed bird community, the number of species per family was similar; however, the number of individuals per family/species differed. We also identified the necessity of CS by confirming the possibility of predicting bird communities using CS-specific data. CS was influenced by various factors, including the perceptions of the survey participants and their level of experience. Therefore, conducting CS after systematic training can facilitate the collection of higher-quality data.

Piloting a Measure of Segregation at the Census Tract Level: Associations with Place and Racial/Ethnic Disparities in Life Expectancy.

This study considers residential segregation as a critical driver of racial/ethnic health disparities and introduces a proxy measure of segregation that estimates the degree of segregation at the census tract level with a metric capturing the overrepresentation of a racialized/ethnic group in a census tract in relation to that group's representation at the city level. Using Dallas, Texas as a pilot city, the measure is used to investigate mean life expectancy at birth for relatively overrepresented Hispanic, non-Hispanic white, non-Hispanic Black, and Asian census tracts and examine for significant differences between mean life expectancy in relatively overrepresented census tracts and that group's mean life expectancy at the state level. Multivariable linear regression analysis was utilized to assess how segregation measured at the census tract level associates with life expectancy across different racialized/ethnic groups, controlling for socioeconomic disparities. This study aimed to expose the need to consider the possibility of neighborhood mechanisms beyond socioeconomic characteristics as an important determinant of health and draw attention to the importance of critically engaging the experience of place in examinations of racial and ethnic health disparities. Multivariable linear regression modeling resulted in significant findings for non-Hispanic Black, non-Hispanic white, and Asian groups, indicating increased census tract-level life expectancy for Black and white residents in highly segregated census tracts and decreased life expectancy for residents of tracts in which the Asian community is overrepresented when compared to state means. Unadjusted models demonstrated socioeconomic inequities between first and fourth quartile census tracts and pointed to the importance of mixed methods in health disparities research and the importance of including the voice of community members to account for places of daily lived experience and people's relationships with them.

Factors associated with medical consumable availability in level 1 facilities in Malawi: a secondary analysis of a facility census.

BACKGROUND: Medical consumable stock-outs negatively affect health outcomes not only by impeding or delaying the effective delivery of services but also by discouraging patients from seeking care. Consequently, supply chain strengthening is being adopted as a key component of national health strategies. However, evidence on the factors associated with increased consumable availability is limited. METHODS: In this study, we used the 2018-19 Harmonised Health Facility Assessment data from Malawi to identify the factors associated with the availability of consumables in level 1 facilities, ie, rural hospitals or health centres with a small number of beds and a sparsely equipped operating room for minor procedures. We estimate a multilevel logistic regression model with a binary outcome variable representing consumable availability (of 130 consumables across 940 facilities) and explanatory variables chosen based on current evidence. Further subgroup analyses are carried out to assess the presence of effect modification by level of care, facility ownership, and a categorisation of consumables by public health or disease programme, Malawi's Essential Medicine List classification, whether the consumable is a drug or not, and level of average national availability. FINDINGS: Our results suggest that the following characteristics had a positive association with consumable availability-level 1b facilities or community hospitals had 64% (odds ratio [OR] 1·64, 95% CI 1·37-1·97) higher odds of consumable availability than level 1a facilities or health centres, Christian Health Association of Malawi and private-for-profit ownership had 63% (1·63, 1·40-1·89) and 49% (1·49, 1·24-1·80) higher odds respectively than government-owned facilities, the availability of a computer had 46% (1·46, 1·32-1·62) higher odds than in its absence, pharmacists managing drug orders had 85% (1·85, 1·40-2·44) higher odds than a drug store clerk, proximity to the corresponding regional administrative office (facilities greater than 75 km away had 21% lower odds [0·79, 0·63-0·98] than facilities within 10 km of the district health office), and having three drug order fulfilments in the 3 months before the survey had 14% (1·14, 1·02-1·27) higher odds than one fulfilment in 3 months. Further, consumables categorised as vital in Malawi's Essential Medicine List performed considerably better with 235% (OR 3·35, 95% CI 1·60-7·05) higher odds than other essential or non-essential consumables and drugs performed worse with 79% (0·21, 0·08-0·51) lower odds than other medical consumables in terms of availability across facilities. INTERPRETATION: Our results provide evidence on the areas of intervention with potential to improve consumable availability. Further exploration of the health and resource consequences of the strategies discussed will be useful in guiding investments into supply chain strengthening. FUNDING: UK Research and Innovation as part of the Global Challenges Research Fund (Thanzi La Onse; reference MR/P028004/1), the Wellcome Trust (Thanzi La Mawa; reference 223120/Z/21/Z), the UK Medical Research Council, the UK Department for International Development, and the EU (reference MR/R015600/1).

A Research Note on Community Resilience Estimates: New U.S. Census Bureau Data With an Application to Excess Deaths From COVID-19.

In this research note, we describe the results of the first validation study of the U.S. Census Bureau's new Community Resilience Estimates (CRE), which uses Census microdata to develop a tract-level vulnerability index for the United States. By employing administrative microdata to link Social Security Administration mortality records to CRE, we show that CRE quartiles provide more stable predictions of COVID-19 excess deaths than single demographic categorizations such as race or age, as well as other vulnerability measures including the U.S. Centers for Disease Control and Prevention's Social Vulnerability Index (SVI) and the Federal Emergency Management Agency's National Risk Index (NRI). We also use machine learning techniques to show that CRE provides more predictive power of COVID-19 excess deaths than standard socioeconomic predictors of vulnerability such as poverty and unemployment, as well as SVI and NRI. We find that a 10-percentage-point increase in a key CRE risk measure is associated with one additional death per neighborhood during the initial outbreak of COVID-19 in the United States. We conclude that, compared with alternative measures, CRE provides a more accurate predictor of community vulnerability to a disaster such as a pandemic.

An Analysis of American Urological Association Census Trends: Earlier Planned Retirement and Practice Pattern Changes May Exacerbate the Urology Workforce Shortage.

OBJECTIVE: To explore how changes in planned retirement age, practice setting, and physician productivity may impact the workforce shortage in urology. METHODS: We compared data between the 2015 and 2022 American Urological Association census, a specialty-wide annual survey which collects data on demographics, practice patterns, and procedures from a representative sample of U.S. urologists. Workforce productivity was measured by the self-reported number of hours worked per week and patients seen per week. A novel formula was developed to demonstrate how planned retirement age and productivity impact the workforce's production capacity. RESULTS: The total number of practicing urologists increased during the period from 2015 to 2022 (11,990 to 13,976), while the mean age of practicing urologists decreased slightly (55.0 to 54.5years; P < .002). During this period, the mean planned age of retirement for all urologists decreased from 68.9years to 67.7 (P < .001). Urologists in solo practice had a significantly higher planned age of retirement at 71.9years (P < .001) as compared to all other practice models. The number of patients seen per week for all urologists decreased from 78.7 to 72.9 (P < .001). The amount of hours worked per week remained relatively constant between the study periods. The maximum possible number of patients seen by the workforce prior to retirement increased by only 2.4% during the study interval. CONCLUSION: Though the U.S. urology workforce is growing and the mean age is decreasing, decreases in planned retirement age and productivity may offset these gains and intensify the physician shortage for U.S. urologists.

Burnout in Urological Education: An In-Depth Study of Residents and Fellows in the 2021 AUA Census.

PURPOSE: Our goal was to explore the current trends in burnout, career choice regret, and well-being needs among urology residents and fellows, with specific emphasis on identifying key factors associated with burnout. MATERIALS AND METHODS: The AUA Workforce Workgroup collaborated with the AUA Data Team to analyze data from the 2021 AUA Census, comprising a total of 243 residents and fellows. Key demographics, benefits and resources, career choice and debt, and burnout levels were analyzed, focusing on variables like gender, PGY (postgraduate year) level, debt burden, and personal health appointments. RESULTS: Overall, 48% of residents and 33% of fellows met criteria for professional burnout, with a higher incidence among PGY-2 residents (70%). Depersonalization was particularly notable, with 74% of residents reporting medium to high levels. Burnout was significantly associated with difficulty attending personal health appointments (52% vs 34%) and lack of access to on-call rooms (54% vs 36%). In contrast, having children during residency was associated with lower burnout levels (30.8% vs 49.1%). Meal plans were ranked as the most desired benefit (32%), followed by ability to attend health appointments during work hours (17%) and paid family leave (16%). Educational debt over $150,000 was carried by 53% of residents and 48% of fellows. Interestingly, burnout rates showed no statistically significant difference in response rates across genders, relationship status, amount of educational debt, presence of paid maternity or paternity leave, and type of childcare arrangements. CONCLUSIONS: Burnout remains a significant issue among urology trainees, with a complex interplay of factors like lack of personal time and provision of call rooms. The alarming rates of depersonalization and exhaustion highlight the urgency of implementing targeted interventions. Enhanced support systems, improved access to health care appointments, provision of call rooms, and debt management programs are recommended to alleviate the growing problem of professional burnout in the field of urology.

Prevalence of menthol cigarette use among adults who smoke from the United States by census division and demographic subgroup, 2002-2020: findings from the International Tobacco Control (ITC) project.

BACKGROUND: Targeted marketing of menthol cigarettes in the US influences disparities in the prevalence of menthol smoking. There has been no analysis of sub-national data documenting differences in use across demographic subgroups. This study estimated trends in the prevalence of menthol use among adults who smoke in the nine US census divisions by sex, age, and race/ethnicity from 2002 to 2020. METHODS: Data from 12 waves of the US ITC Survey were used to estimate the prevalence of menthol cigarette use across census divisions and demographic subgroups using multilevel regression and post-stratification (n = 12,020). Multilevel logistic regression was used to predict the prevalence of menthol cigarette use in 72 cross-classified groups of adults who smoke defined by sex, age, race/ethnicity, and socioeconomic status; division-level effects were fit with a random intercept. Predicted prevalence was weighted by the total number of adults who smoke in each cross-classified group and aggregated to divisions within demographic subgroup. Estimates were validated against the Tobacco Use Supplement to the Current Population Survey (TUS-CPS). RESULTS: Overall modeled prevalence of menthol cigarette use was similar to TUS-CPS estimates. Prevalence among adults who smoke increased in each division from 2002 to 2020. By 2020, prevalence was highest in the Middle (46.3%) and South Atlantic (42.7%) and lowest in the Pacific (25.9%) and Mountain (24.2%) divisions. Prevalence was higher among adults aged 18-29 (vs. 50+) and females (vs. males). Prevalence among non-Hispanic Black people exceeded 80% in the Middle Atlantic, East North Central, West North Central, and South Atlantic in all years and varied most among Hispanic people in 2020 (Pacific: 26.5%, New England: 55.1%). CONCLUSIONS: Significant geographic variation in the prevalence of menthol cigarette use among adults who smoke suggests the proposed US Food and Drug Administration (FDA) menthol cigarette ban will exert differential public health benefits and challenges across geographic and demographic subgroups.

A reassessment of trends and rural-urban/regional differences in the total fertility rate in China, 2000-2020: analyses of the 2020 national census data.

The decline in the total fertility rate (TFR) is a key driver of population change and has important implications for population health and social development. However, China's TFR has been a considerable controversy due to a lack of high-quality data. Therefore, this study used the 2020 national population census of China (NPCC) data and reverse survival method to reassess temporal trends in the TFRs and to reexamine rural-urban differences and regional variations in TFRs from 2000 to 2020 in China. Overall, there were significant gaps between the estimated and reported TFRs before 2020, and the estimated TFRs based on the 2020 NPCC data remained higher than the reported TFRs from government statistics. Although TFRs rebounded shortly in the years after the two-child policy, they have shown a wavelike decline since 2010. Additionally, the estimated TFRs fluctuated below 1.5 children per woman in urban areas compared to above 1.8 in rural areas, but the rural-urban differences continued to decrease. Regarding geographic regional variations, the estimated TFRs in all regions displayed a declining trend during 2010-2020, especially in rural areas. Large decreases of over 25% in TFRs occurred in the north, east, central, and northwest regions. In addition to changing the birth policy, the government and society should adopt comprehensive strategies, including reducing the costs of marriage, childbearing, and child education, as well as promoting work-family balance, to encourage and increase fertility levels.

U.S. Fertility in Life Course Context: A Research Note on Using Census-Held Linked Administrative Records for Geographic and Sociodemographic Subgroup Estimation.

Fertility is a life course process that is strongly shaped by geographic and sociodemographic subgroup contexts. In the United States, scholars face a choice: they can situate fertility in a life course perspective using panel data, which is typically representative only at the national level; or they can attend to subnational contexts using rate schedules, which do not include information on life course statuses. The method and data source we introduce here, Census-Held Linked Administrative Records for Fertility Estimation (CLAR-FE), permits both. It derives fertility histories and rate schedules from U.S. Census Bureau-held data for the nation and by state, racial and ethnic subgroups, and the important life course status of parity. We generate three types of rates for 2000-2020 at the national and state levels by race and ethnicity: age-specific rates and both unconditional and conditional parity- and age-specific rates. Where possible, we compare these rates with those produced by the National Center for Health Statistics. Our new rate schedules illuminate state and racial and ethnic differences in transitions to parenthood, providing evidence of the important subgroup heterogeneity that characterizes the United States. CLAR-FE covers nearly the entire U.S. population and is available to researchers on approved projects through the Census Bureau's Federal Statistical Research Data Centers.