survey estimates
Recently Published Documents


TOTAL DOCUMENTS

196
(FIVE YEARS 58)

H-INDEX

24
(FIVE YEARS 3)

BMJ Open ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. e051427
Author(s):  
Caroline Jeffery ◽  
Marcello Pagano ◽  
Baburam Devkota ◽  
Joseph J Valadez

ObjectiveCombine Health Management Information Systems (HMIS) and probability survey data using the statistical annealing technique (AT) to produce more accurate health coverage estimates than either source of data and a measure of HMIS data error.SettingThis study is set in Bihar, the fifth poorest state in India, where half the population lives below the poverty line. An important source of data, used by health professionals for programme decision making, is routine health facility or HMIS data. Its quality is sometimes poor or unknown, and has no measure of its uncertainty. Using AT, we combine district-level HMIS and probability survey data (n=475) for the first time for 10 indicators assessing antenatal care, institutional delivery and neonatal care from 11 blocks of Aurangabad and 14 blocks of Gopalganj districts (N=6 253 965) in Bihar state, India.ParticipantsBoth districts are rural. Bihar is 82.7% Hindu and 16.9% Islamic.Primary outcome measuresSurvey prevalence measures for 10 indicators, corresponding prevalences using HMIS data, combined prevalences calculated with AT and SEs for each type of data.ResultsThe combined and survey estimates differ by <0.10. The combined and HMIS estimates differ by up to 84.2%, with the HMIS having 1.4–32.3 times larger error. Of 20 HMIS versus survey coverage estimate comparisons across the two districts only five differed by <0.10. Of 250 subdistrict-level comparisons of HMIS versus combined estimates, only 36.4% of the HMIS estimates are within the 95% CI of the combined estimate.ConclusionsOur statistical innovation increases the accuracy of information available for local health system decision making, allows evaluation of indicator accuracy and increases the accuracy of HMIS estimates. The combined estimates with a measure of error better informs health system professionals about their risks when using HMIS estimates, so they can reduce waste by making better decisions. Our results show that AT is an effective method ready for additional international assessment while also being used to provide affordable information to improve health services.


Author(s):  
Hadeel Mohammad Darwish, Muhammad Mazyad Drybati, Mounzer Ha Hadeel Mohammad Darwish, Muhammad Mazyad Drybati, Mounzer Ha

Statistical surveys are usually conducted to obtain data describing a problem in a studied society, and many surveys experience a rise in nonresponse rates, as the rate of nonresponse may affect the bias of the nonresponse in survey estimates. Recent empirical results show instances of nonresponse rate correlation with nonresponse bias, we attempt to translate statistical experiences of nonresponse bias in newly published studies and research into causal models that lead to assumptions about when a lack of response causes bias in estimates. Research studies of the estimates of nonresponse bias show that this bias often exists. The logical question is: what is the advantage of surveys if they suffer from high rates of nonresponse, since post-survey adjustments for nonresponse require additional variables, the answer depends on the nature of the design and the quality of the additional variables.  


ILR Review ◽  
2021 ◽  
pp. 001979392110638
Author(s):  
William A. Darity ◽  
Darrick Hamilton ◽  
Samuel L. Myers ◽  
Gregory N. Price ◽  
Man Xu

Racial differences in effort at work, if they exist, can potentially explain race-based wage/earnings disparities in the labor market. The authors estimate specifications of time spent on non-work activities at work by Black and White males and females with data from the American Time Use Survey. Estimates reveal that trivially small differences occur between non-Hispanic Black and non-Hispanic White males in time spent not working while on the job that disappear entirely when correcting for non-response errors. The findings imply that Black–White male differences in the fraction of the workday spent not working are either not large enough to partially explain the Black–White wage gap, or simply do not exist at all.


Author(s):  
Carol Bibiana Colonia ◽  
Rosanna Camerano-Ruiz ◽  
Andrés Felipe Mora-Salamanca ◽  
Ana Beatriz Vásquez-Rodríguez ◽  
Camilo Alberto Pino-Gutiérrez ◽  
...  

Evidence about the effectiveness of school closures as a measure to control the spread of COVID-19 is controversial. We posit that schools are not an important source of transmission; thus, we analyzed two surveillance methods: a web-based questionnaire and a telephone survey that monitored the impact of the pandemic due to COVID-19 cases in Bogotá, Colombia. We estimated the cumulative incidences for Acute Respiratory Infection (ARI) and COVID-19 for each population group. Then, we assessed the differences using the cumulative incidence ratio (CIR) and 95% confidence intervals (CI95%). The ARI incidence among students was 20.1 times higher when estimated from the telephone survey than from the online questionnaire (CIR: 20.1; CI95% 17.11–23.53). Likewise, the ARI incidence among schoolteachers was 10 times higher in the telephone survey (CIR: 9.8; CI95% 8.3–11.5). the incidence of COVID-19 among schoolteachers was 4.3 times higher than among students in the online questionnarie (CIR: 4.3, CI95%: 3.8–5.0) and 2.1 times higher in the telephone survey (CIR = 2.1, CI95%: 1.8–2.6), and this behavior was also observed in the general population data. Both methods showed a capacity to detect COVID-19 transmission among students and schoolteachers, but the telephone survey estimates were probably closer to the real incidence rate.


PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0260301
Author(s):  
Robert Johnston ◽  
Gaurav Dhamija ◽  
Mudit Kapoor ◽  
Praween K. Agrawal ◽  
Arjan de Wagt

Wasting in children under-five is a form of acute malnutrition, a predictor of under-five child mortality and of increased risk of future episodes of stunting and/or wasting. In India, national estimates of wasting are high compared to international standards with one in five children found to be wasted. National surveys are complex logistical operations and most often not planned or implemented in a manner to control for seasonality. Collection of survey data across differing months across states introduces seasonal bias. Cross-sectional surveys are not designed to collect data on seasonality, thus special methods are needed to analyse the effect of data collection by month. We developed regression models to estimate the mean weight for height (WHZ), prevalence of wasting for every month of the year for an average year and an overall weighted survey estimates controlling for the socio-demographic variation of data collection across states and populations over time. National level analyses show the mean WHZ starts at its highest in January, falls to the lowest in June/August and returns towards peak at year end. The prevalence of wasting is lowest in January and doubles by June/August. After accounting for seasonal patterns in data collection across surveys, the trends are significantly different and indicate a stagnant period followed by a decline in wasting. To avoid biased estimates, direct comparisons of acute malnutrition across surveys should not be made unless seasonality bias is appropriately addressed in planning, implementation or analysis. Eliminating the seasonal variation in wasting would reduce the prevalence by half and provide guidance towards further reduction in acute malnutrition.


2021 ◽  
Author(s):  
Natalie L Edelman ◽  
Peter Simon ◽  
Jackie A Cassell ◽  
Istvan Kiss

Lockdowns have been a key infection control measure for many countries during the COVID-19 pandemic. In England first lockdown, children of single parent households (SPHs) were permitted to move between parental homes. By the second lockdown, SPH support bubbles between households were also permitted, enabling larger within-household networks. We investigated the combined impact of these approaches on household transmission dynamics, to inform policymaking for control and support mechanisms in a respiratory pandemic context. This network modelling study applied percolation theory to a base model of SPHs constructed with population survey estimates of SPH family size. To explore putative impact, varying estimates were applied regarding extent of bubbling and proportion of Different-parentage SPHs (DSPHs) (in which children do not share both the same parents). Results indicate that the formation of giant components (in which Covid-19 household transmission accelerates) are more contingent on DSPHs than on formation of bubbles between SPHs; and that bubbling with another SPH will accelerate giant component formation where one or both are DSPHs. Public health guidance should include supportive measures that mitigate the increased transmission risk afforded by support bubbling among DSPHs. Future network, mathematical and epidemiological studies should examine both independent and combined impact of policies.


2021 ◽  
Vol 50 (Supplement_1) ◽  
Author(s):  
Lizzie Gorrell ◽  
Kevin Monahan ◽  
Chloe Groves ◽  
Claire Sparke ◽  
Sarp Kaya

Abstract Focus of Presentation Australian studies examining muscle-strengthening activity (MSA) are limited and most are focused on resistance/weight training. This study uses the nationally representative Sport Australia AusPlay survey of physical activity participation and classifies activities that use major muscle groups as MSAs, adapting the methodology used in UK health studies. Using this classification, estimates are then made on how many Australian adults are doing MSAs on at least two days a week as per the Australian Physical Activity and Sedentary Behaviour Guidelines. Findings There is some uncertainty about which activities can be considered MSAs. However, evidence suggests that many activities, besides resistance/weight training, can strengthen muscles. Three definitions were used to classify physical activities reported by AusPlay respondents as either Resistance Training Only, Definitely MSAs, or Definitely and/or Potentially MSAs. These were applied to AusPlay 2017–18 data to estimate the proportion of adults who met the MSA guideline. For the primary measure of Definitely MSAs, estimates were higher than those from previous Australian studies. Consistent with previous Australian studies, a higher proportion of men than women, and younger adults than older adults, met the MSA guideline across all activity classifications. Conclusions/Implications Survey estimates of MSA participation depend on which activities are included as MSAs. More rigorous studies are needed to clearly categorise which activities can be considered as MSAs. Key messages A clearer understanding of what constitutes MSAs will improve estimates of how many Australian adults are meeting the MSA guidelines.


2021 ◽  
Vol 13 (16) ◽  
pp. 8915
Author(s):  
Madhuri Sharma

Gender economic parity comprises an integral part of the United Nation’s 17 goals toward attaining sustainable development. Women have historically been confined to feminine occupations associated with lower pay, which have negatively impacted their economic wellbeing. This paper examines gendered dimensions of occupational (dis)parity across US counties and their association with educational attainment. Drawing on five years’ American Community Survey estimates (2015–2019) data from the National Historical Geographic Information System, I conduct descriptive statistical analysis of occupation-based location quotients and education, followed by an in-depth share analysis of 26 gender-based sub-categories of occupations. The correlation analysis provides insights into the multiple dimensions of gendered inequalities. Women’s largest engagements still include sales/office (28.66%), service (21.15%), and education/legal/community-service/arts/media (15.03%)—accounting toward 65% of all employed women in the US. Women majoring in science/engineering and related disciplines are still the lowest, which manifests into their alarmingly lower representations in science/engineering and related occupations. This suggests strategic policy interventions to advance women in STEM education. This analysis, however, also suggests occupational parity for women with a master’s education and above who share almost similar types of relationships with major categories of occupations, even though the coefficients are more favorable for males in managerial jobs.


PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0253375
Author(s):  
Joseph Ouma ◽  
Caroline Jeffery ◽  
Colletar Anna Awor ◽  
Allan Muruta ◽  
Joshua Musinguzi ◽  
...  

Background Model-based small area estimation methods can help generate parameter estimates at the district level, where planned population survey sample sizes are not large enough to support direct estimates of HIV prevalence with adequate precision. We computed district-level HIV prevalence estimates and their 95% confidence intervals for districts in Uganda. Methods Our analysis used direct survey and model-based estimation methods, including Fay-Herriot (area-level) and Battese-Harter-Fuller (unit-level) small area models. We used regression analysis to assess for consistency in estimating HIV prevalence. We use a ratio analysis of the mean square error and the coefficient of variation of the estimates to evaluate precision. The models were applied to Uganda Population-Based HIV Impact Assessment 2016/2017 data with auxiliary information from the 2016 Lot Quality Assurance Sampling survey and antenatal care data from district health information system datasets for unit-level and area-level models, respectively. Results Estimates from the model-based and the direct survey methods were similar. However, direct survey estimates were unstable compared with the model-based estimates. Area-level model estimates were more stable than unit-level model estimates. The correlation between unit-level and direct survey estimates was (β1 = 0.66, r2 = 0.862), and correlation between area-level model and direct survey estimates was (β1 = 0.44, r2 = 0.698). The error associated with the estimates decreased by 37.5% and 33.1% for the unit-level and area-level models, respectively, compared to the direct survey estimates. Conclusions Although the unit-level model estimates were less precise than the area-level model estimates, they were highly correlated with the direct survey estimates and had less standard error associated with estimates than the area-level model. Unit-level models provide more accurate and reliable data to support local decision-making when unit-level auxiliary information is available.


Author(s):  
Connor Donegan ◽  
Yongwan Chun ◽  
Daniel A. Griffith

Epidemiologists and health geographers routinely use small-area survey estimates as covariates to model areal and even individual health outcomes. American Community Survey (ACS) estimates are accompanied by standard errors (SEs), but it is not yet standard practice to use them for evaluating or modeling data reliability. ACS SEs vary systematically across regions, neighborhoods, socioeconomic characteristics, and variables. Failure to consider probable observational error may have substantial impact on the large bodies of literature relying on small-area estimates, including inferential biases and over-confidence in results. The issue is particularly salient for predictive models employed to prioritize communities for service provision or funding allocation. Leveraging the tenets of plausible reasoning and Bayes’ theorem, we propose a conceptual framework and workflow for spatial data analysis with areal survey data, including visual diagnostics and model specifications. To illustrate, we follow Krieger et al.’s (2018) call to routinely use the Index of Concentration at the Extremes (ICE) to monitor spatial inequalities in health and mortality. We construct and examine SEs for the ICE, use visual diagnostics to evaluate our observational error model for the ICE, and then estimate an ICE–mortality gradient by incorporating the latter model into our model of sex-specific, midlife (ages 55–64), all-cause United States county mortality rates. We urge researchers to consider data quality as a criterion for variable selection prior to modeling, and to incorporate data reliability information into their models whenever possible.


Sign in / Sign up

Export Citation Format

Share Document