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.

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.

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.

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).

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.

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.

Educational inequalities in all-cause and cause-specific mortality in Japan: national census-linked mortality data for 2010-15.

BACKGROUND: Due to the lack of a national mortality inequality monitoring framework, the overall picture in Japan remains unclear. Here, we investigated educational inequalities in mortality and their cause-specific contribution in Japan. METHOD: Data were obtained by linking the 2010 Japanese population census and death records between 1 October 2010 and 30 September 2015. We included 7 984 451 Japanese people aged 30-79 years who had a unique 'matching key' generated by sex, birth year/month, address (municipality), marital status and age of spouse (9.9% of the total census population). We computed population-weighted all-cause and cause-specific age-standardized mortality rates (ASMRs) by education level. In addition, we calculated the slope index of inequality (SII), relative index inequality (RII) by education level, and population attributable fraction (PAF) referenced with the highest education (e.g. university graduation). RESULTS: Individuals with less education had higher all-cause and cause-specific ASMRs than highly educated individuals. All-cause SII (per 100 000 person-years) values were 433 (95% CI: 410-457) for men and 235 (95% CI: 217-252) for women. RII values were 1.48 (95% CI: 1.45-1.51) for men and 1.47 (95% CI: 1.43-1.51) for women. Estimated PAFs, excess premature deaths caused by educational inequalities, were 11.6% for men and 16.3% for women, respectively. Cerebrovascular diseases, ischaemic heart diseases and lung cancer were the major contributors to mortality inequalities for both sexes. CONCLUSIONS: This first census-based comprehensive report on cause-specific educational mortality inequalities suggested that differences in unfavourable health risk factors by educational background might be associated with these inequalities in Japan.

Survival analysis under imperfect record linkage using historic census data.

BACKGROUND: Advancements in linking publicly available census records with vital and administrative records have enabled novel investigations in epidemiology and social history. However, in the absence of unique identifiers, the linkage of the records may be uncertain or only be successful for a subset of the census cohort, resulting in missing data. For survival analysis, differential ascertainment of event times can impact inference on risk associations and median survival. METHODS: We modify some existing approaches that are commonly used to handle missing survival times to accommodate this imperfect linkage situation including complete case analysis, censoring, weighting, and several multiple imputation methods. We then conduct simulation studies to compare the performance of the proposed approaches in estimating the associations of a risk factor or exposure in terms of hazard ratio (HR) and median survival times in the presence of missing survival times. The effects of different missing data mechanisms and exposure-survival associations on their performance are also explored. The approaches are applied to a historic cohort of residents in Ambler, PA, established using the 1930 US census, from which only 2,440 out of 4,514 individuals (54%) had death records retrievable from publicly available data sources and death certificates. Using this cohort, we examine the effects of occupational and paraoccupational asbestos exposure on survival and disparities in mortality by race and gender. RESULTS: We show that imputation based on conditional survival results in less bias and greater efficiency relative to a complete case analysis when estimating log-hazard ratios and median survival times. When the approaches are applied to the Ambler cohort, we find a significant association between occupational exposure and mortality, particularly among black individuals and males, but not between paraoccupational exposure and mortality. DISCUSSION: This investigation illustrates the strengths and weaknesses of different imputation methods for missing survival times due to imperfect linkage of the administrative or registry data. The performance of the methods may depend on the missingness process as well as the parameter being estimated and models of interest, and such factors should be considered when choosing the methods to address the missing event times.

The effect of educational attainment on birthrate in Japan: an analysis using the census and the vital statistics from 2000 to 2020.

BACKGROUND: In Japan, difference in birth rates depending on educational attainment has not been investigated. This study aimed to reveal birth rates in Japan depending on the highest level of educational attainment and their trends over the years using nationwide government statistics data. METHODS: Individual-level data from Vital Statistics and the Census from 2000, 2010, and 2020 were used for birth and population data, respectively. Data linkage was conducted for males and females in the Census and fathers and mothers in the Vital Statistics using information about gender, household, nationality, marital status, birth year, birth month, prefecture, and municipality for individuals. The birth rate was calculated by gender, a five-year age group, the highest level of educational attainment achieved, and year. In addition, the slope index of inequality (SII) and relative index of inequality (RII) were calculated to evaluate the degree of inequality in birth rates, depending on the educational attainment. RESULTS: Birth rates were higher in persons with lower educational attainment compared to those with a higher educational attainment among males and females in their twenties, while they tended to be higher in persons with higher educational attainment among those in their thirties and forties. Additionally, an increase in the birth rate from 2000 to 2020 was the largest in university graduates among males aged 25-49 years and women aged 30-49 years, and a decrease in the birth rate was the smallest in university graduates among males and females aged 20-24 years. As a result, SII and RII increased from 2000 to 2020 among males and females in their thirties and forties. CONCLUSIONS: In conclusion, persons with higher educational attainment tended to have a relatively favorable trend in the birth rate compared with persons with lower educational attainment in recent decades. It suggested that enhanced administrative support for individuals with lower educational attainment or lower socioeconomic status may be required to ameliorate the declining birth rate in Japan.

Evaluating Methods for Mapping Historical Redlining to Census Tracts for Health Equity Research.

Neighborhood characteristics including housing status can profoundly influence health. Recently, increasing attention has been paid to present-day impacts of "redlining," or historic area classifications that indicated less desirable (redlined) areas subject to decreased investment. Scholarship of redlining and health is emerging; limited guidance exists regarding optimal approaches to measuring historic redlining in studies of present-day health outcomes. We evaluated how different redlining approaches (map alignment methods) influence associations between redlining and health outcomes. We first identified 11 existing redlining map alignment methods and their 37 logical extensions, then merged these 48 map alignment methods with census tract life expectancy data to construct 9696 linear models of each method and life expectancy for all 202 redlined cities. We evaluated each model's statistical significance and R2 values and compared changes between historical and contemporary geographies and populations using Root Mean Squared Error (RMSE). RMSE peaked with a normal distribution at 0.175, indicating persistent difference between historical and contemporary geographies and populations. Continuous methods with low thresholds provided higher neighborhood coverage. Weighting methods had more significant associations, while high threshold methods had higher R2 values. In light of these findings, we recommend continuous methods that consider contemporary population distributions and mapping overlap for studies of redlining and health. We developed an R application {holcmapr} to enable map alignment method comparison and easier method selection.

Counting culturally and linguistically diverse (CALD) children in Australian health research: Does it matter how we count?

OBJECTIVE: To describe how culturally and linguistically diverse (CALD) children are identified and enumerated in routine data collections and in child health research in Australia. METHODS: Descriptive analysis, where different definitions of CALD were applied to the 2021 Australian Census to measure the size of the CALD population of Australian children aged 0 to 17 years. Narrative review of the Australian child health literature to examine how CALD children were defined. RESULTS: Applying various definitions to the 2021 Census, the estimated proportion of CALD children aged 0 to 17 ranged from 6.3% to 43%. The most commonly applied CALD indicators were language background other than English and being born overseas. CONCLUSIONS: There is no consensus on how CALD is defined in Australian child health research. Application of different CALD indicators can generate up to seven-fold differences in estimates of who counts as being a CALD child. IMPLICATIONS FOR PUBLIC HEALTH: If we are to advance health and well-being equity for CALD children, we need a more consistent approach to understanding which children are counted as CALD.

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.

Assessing the impact of socio-demographics and farming activity on ward-level mortality patterns using farm and population decennial censuses.

INTRODUCTION AND OBJECTIVE: Farmers experience a specific set of unique dangers, which increases their risk of mortality compared with any other occupation. This study hypothesised that Northern Ireland's (NIs) agriculturally saturated Wards have a higher risk of mortality compared against non-agriculturally based Wards. DESIGN: The Population Census and Farm Census information were downloaded from the Northern Ireland Neighbourhood Service (NINIS) online depository to compile three mortality-based data sets (2001, 2011, pooled data sets). Assessing the impact of socio-demographics and farming activity on Ward-level mortality patterns using farm and population decennial censuses. This study analysed all 582 Ward areas of NI, which enclosed the entire populace of the country in 2001 and 2011. FINDINGS: Path analysis was utilised to examine direct and indirect paths linked with mortality within two census years (2001; 2011), alongside testing pathways for invariance between census years (pooled data set). Ward-level results provided evidence for exogenous variables to mortality operating through three/four endogenous variables via: (i) direct effects (age), (ii) summed indirect effects (age; males; living alone; farming profit; and deprivation) and (iii) total effects (age; males; living alone; and deprivation). Multi-group results cross-validated these cause-and-effect relationships relating to mortality. DISCUSSION AND CONCLUSION: This study demonstrated that farming intensity scores, farming profits and socio-demographics' influence on mortality risk in a Ward were dependent on the specific social-environmental characteristics within that area. In line with earlier area level research, results support the aggregated interpretation that higher levels of farming activity within a Ward increase the risk of mortality within those Wards of NI. This was an essential study to enable future tailoring of new strategies and upgrading of current policies to bring about significant mortality risk change at local level.

Peritoneal Dialysis in Italy: the 8th GPDP-SIN Census 2022 - 2nd Part: the Centers.

Objectives. The results are presented of the 8th National Census (Cs-22) of the Peritoneal Dialysis Project Group of the Italian Society of Nephrology relating to the characteristics of the Centers in Italy which used PD in 2022. Materials and methods. The 227 non-pediatric centers which used Peritoneal Dialysis (PD) in 2022 took part. The data requested were sent in aggregate form. For the first time, the resources available and training were investigated as well as home visits. The Centers have been divided into Quartiles according to the number of prevalent PD patients at 31/12/2022. Results. Centers with a smaller PD program (<9 pts) are characterized by 1. smaller overall size - 2. fewer personnel (doctors/nurses) dedicated to PD - 3. greater recourse to external personnel for training - 4. Less incremental prescription and evaluation of peritoneal permeability - 5. higher drop-out to HD in particular for choice/impossibility to continue and for adequacy/catheter-related issues. A lower peritonitis rate was recorded in Centers with a more extensive PD program (≥25 pts). Home visits are carried out regularly by a small minority of Centers. Conclusions. The analysis shows an association between size of Center PD program and available resources, PD modality and outcome.

Assessment of census-tract level socioeconomic position as a modifier of the relationship between short-term PM2.5 exposure and cardiovascular emergency department visits in Missouri.

INTRODUCTION: Ambient particulate matter ≤ 2.5 µm in aerodynamic diameter (PM2.5) exposure elevates the risk for cardiovascular disease morbidity (CVDM). The aim of this study is to characterise which area-level measures of socioeconomic position (SEP) modify the relationship between PM2.5 exposure and CVDM in Missouri at the census-tract (CT) level. METHODS: We use individual level Missouri emergency department (ED) admissions data (n=3 284 956), modelled PM2.5 data, and yearly CT data from 2012 to 2016 to conduct a two-stage analysis. Stage one uses a case-crossover approach with conditional logistic regression to establish the baseline risk of ED visits associated with IQR changes in PM2.5. In the second stage, we use multivariate metaregression to examine how CT-level SEP modifies the relationship between ambient PM2.5 exposure and CVDM. RESULTS: We find that overall, ambient PM2.5 exposure is associated with increased risk for CVDM. We test effect modification in statewide and urban CTs, and in the warm season only. Effect modification results suggest that among SEP measures, poverty is most consistently associated with increased risk for CVDM. For example, across Missouri, the highest poverty CTs are at an elevated risk for CVDM (OR=1.010 (95% CI 1.007 to 1.014)) compared with the lowest poverty CTs (OR=1.004 (95% CI 1.000 to 1.008)). Other SEP modifiers generally display an inconsistent or null effect. CONCLUSION: Overall, we find some evidence that area-level SEP modifies the relationship between ambient PM2.5 exposure and CVDM, and suggest that the relationship between air-pollution, area-level SEP and CVDM may be sensitive to spatial scale.

Testing methods to estimate population size for wastewater treatment plants using census data: Implications for wastewater-based epidemiology.

In wastewater-based epidemiology (WBE), wastewater loads are commonly reported as a per capita value. Census population counts are often used to obtain a population size to normalise wastewater loads. However, the methods used to calculate the population size of wastewater treatment plants (WWTPs) from census data are rarely reported in the WBE literature. This is problematic because the geographical extents of wastewater catchments and census area units rarely align perfectly with each other and exist at different spatial scales. This complicates efforts to estimate the number of people serviced by WWTPs in these census area units. This study compared four geospatial methods to combine wastewater catchment areas and census area units to calculate the census population size of wastewater treatment plants. These methods were applied nationally to WWTPs across New Zealand. Population estimates varied by up to 73 % between the methods, which could skew comparisons of per capita wastewater loads between sites. Variability in population estimates (relative standard deviation, RSD) was significantly higher in smaller catchments (rs = -0.727, P < .001), highlighting the importance of method selection in smaller sites. Census population estimates were broadly similar to those provided by wastewater operators, but significant variation was observed for some sites (ranging from 42 % lower to 78 % higher, RSD = 262 %). We present a widely applicable method to calculate population size from census, which involves disaggregating census area units by individual properties. The results reinforce the need for transparent reporting to maintain confidence in the comparison of WBE across sites and studies.