Tests of forecast accuracy and bias for county population projections

Outpatient Hysteroscopic Polypectomy-A Retrospective Study Comparing Rigid and Semirigid Office Hysteroscopes

Endometrial polyps are one of the most common pathological conditions in gynecology. Hysteroscopy is the gold standard for the diagnosis and treatment of endometrial polyps. The purpose of this multicenter, retrospective study was to compare patients' pain perception during an operative hysteroscopic endometrial polypectomy in an outpatient setting with two different hysteroscopes (rigid and semirigid) and to identify some clinical and intraoperative characteristics that are related to worsening pain during the procedure. We included women that underwent, at the same time as an diagnostic hysteroscopy, the complete removal of an endometrial polyp (using the see-and-treat strategy) without any kind of analgesia. A total of 166 patients were enrolled, of which 102 patients underwent a polypectomy with a semirigid hysteroscope and 64 patients underwent the procedure with a rigid hysteroscope. No differences were found during the diagnostic step; on the contrary, after the operative procedure, a statistically significant greater degree of pain was reported when the semirigid hysteroscope was used. Cervical stenosis and menopausal status were risk factors for pain both in the diagnostic step and in the operative one. Our results confirm that operative hysteroscopic endometrial polypectomy in an outpatient setting is an effective, safe, and well-tolerated procedure and indicate that it might be better tolerated if a rigid rather than semirigid instrument is used.

Exploiting the Bayesian approach to derive counts of married women of reproductive age across Cameroon for healthcare planning, 2000-2030

Estimates of married women of reproductive age (MWRA) are needed for policy decisions to enhance reproductive health. Given the unavailability in Cameroon, this study aimed to derive MWRA counts by regions and divisions from 2000 to 2030. Data included 1976, 1987, and 2005 censuses with 606,542 women, five Demographic and Health Surveys from 1991 to 2018 with 48,981 women, and United Nations World Population Prospects from 1976 to 2030. Bayesian models were used in estimating fertility rates, net-migration, and finally, MWRA counts. The total MWRA population in Cameroon was estimated to increase from 2,260,665 (2,198,569-2,352,934) to 6,124,480 (5,862,854-6,482,921), reflecting a 5.7 (5.2-6.2) percentage points (%p) annual rise from 2000-2030. The Centre and Far North regions host the largest numbers, projected to reach 1,264,514 (1,099,373-1,470,021) and 1,069,814 (985,315-1,185,523), respectively, in 2030. The highest divisional-level increases are expected in Mfoundi [14.6%p (11.2-18.8)] and Bénoué [14.9%p (11.1-20.09). This study's findings, showing varied regional- and divisional-level estimates of and trends in MWRA counts should set a baseline for determining the demand for programmes such as family planning, and the scaling of relevant resources sub-nationally.

Evaluation and analysis of the projected population of China

The population has a significant influence on economic growth, energy consumption, and climate change. Many scholars and organizations have published projections for China's future population due to its substantial population amounts. However, these projections have not been evaluated or analyzed, which may lead confusion to extensional studies based on these datasets. This manuscript compares several China's projection datasets at multiscale and analyzes the impacting factors affecting projection accuracy. The results indicate that the slow of actual population growth rates from 2017 is earlier than most datasets projected. Therefore, the turning point of population decline probably comes rapidly before these datasets expected during 2024 and 2034. Furthermore, the projections do not reveal the population decline from 2010 in the Northeast provinces such as Jilin and Heilongjiang, and underrate the population increase in the southern provinces such as Guangdong and Chongqing. According to the results of regression models, the rate of population changes and the number of migrations people play a significant role in projection accuracy. These findings provide meaningful guidance for scholars to understand the uncertainty of those projection datasets. Moreover, for researchers performing population projections, our discoveries provide insights to increase the projection accuracy.

Methods for Small Area Population Forecasts: State-of-the-Art and Research Needs

Small area population forecasts are widely used by government and business for a variety of planning, research and policy purposes, and often influence major investment decisions. Yet, the toolbox of small area population forecasting methods and techniques is modest relative to that for national and large subnational regional forecasting. In this paper, we assess the current state of small area population forecasting, and suggest areas for further research. The paper provides a review of the literature on small area population forecasting methods published over the period 2001–2020. The key themes covered by the review are extrapolative and comparative methods, simplified cohort-component methods, model averaging and combining, incorporating socioeconomic variables and spatial relationships, ‘downscaling’ and disaggregation approaches, linking population with housing, estimating and projecting small area component input data, microsimulation, machine learning, and forecast uncertainty. Several avenues for further research are then suggested, including more work on model averaging and combining, developing new forecasting methods for situations which current models cannot handle, quantifying uncertainty, exploring methodologies such as machine learning and spatial statistics, creating user-friendly tools for practitioners, and understanding more about how forecasts are used.

An Overview of Population Projections—Methodological Concepts, International Data Availability, and Use Cases

Population projections serve various actors at subnational, national, and international levels as a quantitative basis for political and economic decision-making. Usually, the users are no experts in statistics or forecasting and therefore lack the methodological and demographic background to completely understand methods and limitations behind the projections they use to inform further analysis. Our contribution primarily targets that readership. Therefore, we give a brief overview of different approaches to population projection and discuss their respective advantages and disadvantages, alongside practical problems in population data and forecasting. Fundamental differences between deterministic and stochastic approaches are discussed, with special emphasis on the advantages of stochastic approaches. Next to selected projection data available to the public, we show central areas of application of population projections, with an emphasis on Germany.

Population projection accuracy: The impacts of sociodemographics, accessibility, land use, and neighbour characteristics

Population projection is essential to governments, businesses, and research communities for many purposes. Although projection performance is often evaluated, we know very little about what factors affect projection accuracy. It is important to understand these factors in order to utilize the projections knowledgeably. This study fills this gap in the literature by comprehensively investigating the possible factors associated with population projection accuracy in 2010 for the continental US counties. The results indicate that the counties whose populations are more predictable tend to be desirable places-places with abundant employment opportunities, reliable public transportation infrastructure, easy access to work, and/or high land development potential; their neighboring counties tend to have a well-educated population and a higher income level. Also, projection accuracy is highly spatially associated. The findings provide important insights for population projection users to understand the characteristics of counties and their neighboring counties associated with their projection accuracy.

Forecast Accuracy and Uncertainty of Australian Bureau of Statistics State and Territory Population Projections

Errors from past rounds of population projections can provide both diagnostic information to improve future projections as well as information for users on the likely uncertainty of current projections. This paper assesses the forecast accuracy of official Australian Bureau of Statistics (ABS) population projections for the states and territories of Australia and is the first major study to do so. For the states and territories, it is found that, after 10-year projection durations, absolute percentage errors lie between about 1% and 3% for the states and around 6% for the territories. Age-specific population projections are also assessed. It is shown that net interstate migration and net overseas migration are the demographic components of change which contributed most to forecast error. The paper also compares ABS projections of total population against simple linear extrapolation, finding that, overall, ABS projections just outperformed extrapolation. No identifiable trend in accuracy over time is detected. Under the assumption of temporal stability in the magnitude of error, empirical prediction intervals are created from past errors and applied to the current set of ABS projections. The paper concludes with a few ideas for future projection rounds.

Evaluating Population Forecast Accuracy: A Regression Approach Using County Data

Many studies have evaluated the impact of differences in population size and growth rate on population forecast accuracy. Virtually all these studies have been based on aggregate data; that is, they focused on average errors for places with particular size or growth rate characteristics. In this study, we take a different approach by investigating forecast accuracy using regression models based on data for individual places. Using decennial census data from 1900 to 2000 for 2,482 counties in the US, we construct a large number of county population forecasts and calculate forecast errors for 10- and 20-year horizons. Then, we develop and evaluate several alternative functional forms of regression models relating population size and growth rate to forecast accuracy; investigate the impact of adding several other explanatory variables; and estimate the relative contributions of each variable to the discriminatory power of the models. Our results confirm several findings reported in previous studies but uncover several new findings as well. We believe regression models based on data for individual places provide powerful but under-utilized tools for investigating the determinants of population forecast accuracy.

Can knowledge improve population forecasts at subcounty levels?

Recent developments in urban and regional planning require more accurate population forecasts at subcounty levels, as well as a consideration of interactions among population growth, traffic flow, land use, and environmental impacts. However, the extrapolation methods, currently the most often used demographic forecasting techniques for subcounty areas, cannot meet the demand. This study tests a knowledge-based regression approach, which has been successfully used for forecasts at the national level, for subcounty population forecasting. In particular, this study applies four regression models that incorporate demographic characteristics, socioeconomic conditions, transportation accessibility, natural amenities, and land development to examine the population change since 1970 and to prepare the 1990-based forecast of year 2000 population at the minor civil division level in Wisconsin. The findings indicate that this approach does not outperform the extrapolation projections. Although the regression methods produce more precise projections, the least biased projections are often generated by one of the extrapolation techniques. The performance of the knowledge-based regression methods is discounted at subcounty levels by temporal instability and the scale effect. The regression coefficients exhibit a statistically significant level of temporal instability across the estimation and projection periods and tend to change more rapidly at finer geographic scales.

Empirical Prediction Intervals for County Population Forecasts

Population forecasts entail a significant amount of uncertainty, especially for long-range horizons and for places with small or rapidly changing populations. This uncertainty can be dealt with by presenting a range of projections or by developing statistical prediction intervals. The latter can be based on models that incorporate the stochastic nature of the forecasting process, on empirical analyses of past forecast errors, or on a combination of the two. In this article, we develop and test prediction intervals based on empirical analyses of past forecast errors for counties in the United States. Using decennial census data from 1900 to 2000, we apply trend extrapolation techniques to develop a set of county population forecasts; calculate forecast errors by comparing forecasts to subsequent census counts; and use the distribution of errors to construct empirical prediction intervals. We find that empirically-based prediction intervals provide reasonably accurate predictions of the precision of population forecasts, but provide little guidance regarding their tendency to be too high or too low. We believe the construction of empirically-based prediction intervals will help users of small-area population forecasts measure and evaluate the uncertainty inherent in population forecasts and plan more effectively for the future.

On the utility of population forecasts

Many customers demand population forecasts, particularly for small areas. Although the forecast evaluation literature is extensive, it is dominated by a focus on accuracy. We go beyond accuracy by examining the concept of forecast utility in an evaluation of a sample of 2,709 counties and census tracts. Wefind that forecasters provide “value-added” knowledge for areas experiencing rapid change or areas with relatively large populations. For other areas, reduced value is more common than added value. Our results suggest that new forecasting strategies and methods such as composite modeling may substantially improve forecast utility.

An evaluation of population projection errors for census tracts

"In this article we evaluate the accuracy and bias of projections of total population and population by age group for census tracts in three counties in Florida. We use [U.S. census] data from 1970 and 1980 and several simple extrapolation techniques to produce projections for 1990; we then compare these projections with 1990 census counts and evaluate the differences. For the total sample, we find mean absolute errors of 17%-20% for the three most accurate techniques for projecting total population and find no indication of overall bias. For individual age groups, mean absolute errors range from 20%-29%." This is a revised version of a paper presented at the 1993 Annual Meeting of the Population Association of America.

An Empirical Analysis of the Effect of Length of Forecast Horizon on Population Forecast Errors

Many studies have found that population forecast errors generally increase with the length of the forecast horizon, but none have examined this relationship in detail. Do errors grow linearly, exponentially, or in some other manner as the forecast horizon becomes longer? Does the error-horizon relationship differ by forecasting technique, launch year, size of place, or rate of growth? Do alternative measures of error make a difference? In this article we address these questions using two simple forecasting techniques and population data from 1900 to 1980 for states in the United States. We find that in most instances there is a linear or nearly linear relationship between forecast accuracy and the length of the forecast horizon, but no consistent relationship between bias and the length of the horizon. We believe that these results provide useful information regarding the nature of population forecast errors.

The relationship between the length of the base period and population forecast errors

"The base period of a population forecast is the time period from which historical data are collected for the purpose of forecasting future population values. The length of the base period is one of the fundamental decisions made in preparing population forecasts, yet very few studies have investigated the effects of this decision on population forecast errors. In this article the relationship between the length of the base period and population forecast errors is analyzed, using three simple forecasting techniques and data from 1900 to 1980 for states in the United States. It is found that increasing the length of the base period up to 10 years improves forecast accuracy, but that further increases generally have little additional effect. The only exception to this finding is long-range forecasts of rapidly growing states, in which a longer base period substantially improves forecast accuracy for two of the forecasting techniques."

A graphic representation of projection accuracy

The author examines "the problem of measuring projection accuracy. Experience with applying the accuracy measure 'quality of prediction', as proposed by Keyfitz, leads the author to a critical evaluation and elaboration of this accuracy measure. Time series of prediction quality values may show a remarkable temporal instability, partly depending on the chosen bench mark, which seriously hinders interpretation. This interpretation problem may be solved by an easily applicable graphical solution, a convenient short-circuit device to assess a projection's accuracy without restrictions as to size of population or length of projection period." The concepts are illustrated with data used for population forecasts for the Netherlands during the 1970s. (SUMMARY IN FRE)

Stability over time in the distribution of population forecast errors

A number of studies in recent years have investigated empirical approaches to the production of confidence intervals for population projections. The critical assumption underlying these approaches is that the distribution of forecast errors remains stable over time. In this article, we evaluate this assumption by making population projections for states for a number of time periods during the 20th century, comparing these projections with census enumerations to determine forecast errors, and analyzing the stability of the resulting error distributions over time. These data are then used to construct and test empirical confidence limits. We find that in this sample the distribution of absolute percentage errors remained relatively stable over time and data on past forecast errors provided very useful predictions of future forecast errors.