18 research outputs found
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Evaluating Gross vs. Net Migration Rates In a County-Level Component Model of Population Change
This paper evaluates the accuracy of county-level population estimates and forecasts under three different methods for estimating the domestic migration in a components-of-change framework. The first is a net-migration approach similar to that used by the U.S. Census Bureau and by many state data centers. While common, the net migration assumption has been widely criticized for not accurately reflecting the population ‘at risk’ of migrating into a county. The other two methods follow a gross migration approach whereby in- and out-migration are added separately into the population change equation. The simple gross migration approach estimates domestic in-migration to each county from the rest of the nation as a whole. The multiregional gross migration model examines flows between specific pairs of counties and adds these together to measure total in-migration. Otherwise, the population estimates models are identical – allowing us to isolate differences in population estimates due solely to how the domestic migration component is estimated.
We evaluate the accuracy of the three migration approaches against the county household population counts of the 2010 Decennial Census using a variety of common measures of predictive accuracy. We find that the simple gross migration model typically produces the smallest forecast errors. However, this is followed closely by the net migration approach, whose average forecast errors exceed the simple gross model by only .2 percentage points. Despite its far greater complexity the multiregional model produces the highest average errors of all three approaches with an average absolute error .7 percentage points higher than the net migration model. This is due largely to a higher proportion of extreme errors —counties where the model produces an average in excess of five or ten percent greater than the actual census counts. We suspect that this is due to measurement error in the Internal Revenue Service migration data, which may be more influential when calculated for specific pairs of counties but has less noticeable impact when distributed across the entire nation (i.e. the simple gross migration approach) or when in and out-migration are subtracted from one another (i.e. the net migration approach). Although producing higher errors when averaged over all counties, the multiregional model still produces the lowest errors for the greatest number of counties.
All three models produce their most reliable estimates for large counties and the greatest error for the smallest counties—places where even small differences can greatly influence year to year changes in migration rates. The simple gross migration approach is generally preferred among mid-sized and larger counties. The multiregional model is typically favored among counties with fewer than 20,000 persons. Counties experiencing rapid decline or growth are also notoriously difficult to estimate, regardless of method. Rapidly growing counties tend to be overestimated, most notably so in the case of the multiregional model which has a natural upward bias to begin with. However, the multiregional model tends to do a little better than the others at estimating population in cases of recent decline. The simple gross migration model is generally preferred for rapidly growing counties. The key exception is among fast growing small counties, which are favored by a multiregional approach.
This project was funded by:
The United States Census Bureau
For Services in Support of the U.S. Census Bureau’s 2010 Estimates Evaluation
Work Order: YA132310SE038
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Summary Report: Population Estimates in Massachusetts: A Report to the Secretary of the Commonwealth
This report summarizes the 2009 estimates results from the UMass Donahue Institute’s Population
Estimates Program (UMDI-PEP). These population estimates are developed in tandem with the
Donahue Institute’s data collection efforts, namely our group quarters and housing unit surveys.
There are several reasons why it is necessary for the Commonwealth of Massachusetts to develop its own population estimates. First, county and sub-county population estimates are a key resource for state and local governments, non-profits, and the private sector which use these estimates to prepare reports, grant applications, business plans, and state and federal compliance documents. At present, public agencies in Massachusetts develop their own estimates on a purely ad-hoc basis or rely upon somewhat questionable estimates from the U.S. Census Bureau that have not been vetted by experts that understand the local demography of the Commonwealth. Secondly, the process of generating population estimates helps UMDI evaluate the quality of the information collected through our surveys. Our population estimates provide an early look at how the new survey data will affect official
Census estimates and help us prioritize communities that are the best candidates for challenging official Census estimates. Lacking such checks, the Census Bureau has been prone to undercount the Massachusetts population. In 2008 alone, Donahue Institute supported challenges added population that could translate into between 33.09 million of federal resources.1 Lastly, developing our own estimates helps to identify the forces driving population change, whether through changes in migration, births, or deaths. Armed with this knowledge, state and local policymakers can address the policy challenges posed by demographic change in a more informed and proactive manner.
We estimate the 2009 population of the Commonwealth of Massachusetts at 6.64 million persons—a 4.4% increase from the last decennial census in 2000. This is 51,873 more persons than estimated by the U.S. Census Bureau for 2009. Much of this gain is a direct consequence of UMDI-PEP’s group quarters (GQ) and housing unit review (HUR) efforts. For the Group Quarters Review project, UMDIPEP collects and submits to the U.S. Census Bureau updated resident counts for GQ facilities. For HUR, the program collects and reviews building permits, mobile home placements and housing unit loss data for each town and city in Massachusetts to estimate the housing stock in the state and counties. The Bureau does not collect housing unit loss data directly from the towns (as it does with the building permits); instead it calculates a loss-by-age-of-structure rate for the U.S. as a whole and then applies this rate to all regions. New England\u27s housing stock is much older than the national average, so this national rate does not correctly reflect the situation in the region. In 2009, we found that the Census Bureau had overestimated the demolitions and therefore underestimated the number of housing units and population for some towns and cities in Massachusetts. Also, some municipalities in the state have actually increased their housing stock due to “adaptive reuse” of older buildings, which was also a component that the Census Bureau routinely missed.
The Census Bureau’s official population estimates for 2009 incorporate data collected by the Donahue Institute on housing units, and may lead to revisions in their official estimates for past years
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Using School-Age Populations to Identify Hard-to- Count Populations: A Report to the Secretary of the Commonwealth
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Long-term Population Projections for Massachusetts Regions and Municipalities
The impact of surgical delay on resectability of colorectal cancer: An international prospective cohort study
AIM: The SARS-CoV-2 pandemic has provided a unique opportunity to explore the impact of surgical delays on cancer resectability. This study aimed to compare resectability for colorectal cancer patients undergoing delayed versus non-delayed surgery. METHODS: This was an international prospective cohort study of consecutive colorectal cancer patients with a decision for curative surgery (January-April 2020). Surgical delay was defined as an operation taking place more than 4 weeks after treatment decision, in a patient who did not receive neoadjuvant therapy. A subgroup analysis explored the effects of delay in elective patients only. The impact of longer delays was explored in a sensitivity analysis. The primary outcome was complete resection, defined as curative resection with an R0 margin. RESULTS: Overall, 5453 patients from 304 hospitals in 47 countries were included, of whom 6.6% (358/5453) did not receive their planned operation. Of the 4304 operated patients without neoadjuvant therapy, 40.5% (1744/4304) were delayed beyond 4 weeks. Delayed patients were more likely to be older, men, more comorbid, have higher body mass index and have rectal cancer and early stage disease. Delayed patients had higher unadjusted rates of complete resection (93.7% vs. 91.9%, P = 0.032) and lower rates of emergency surgery (4.5% vs. 22.5%, P < 0.001). After adjustment, delay was not associated with a lower rate of complete resection (OR 1.18, 95% CI 0.90-1.55, P = 0.224), which was consistent in elective patients only (OR 0.94, 95% CI 0.69-1.27, P = 0.672). Longer delays were not associated with poorer outcomes. CONCLUSION: One in 15 colorectal cancer patients did not receive their planned operation during the first wave of COVID-19. Surgical delay did not appear to compromise resectability, raising the hypothesis that any reduction in long-term survival attributable to delays is likely to be due to micro-metastatic disease