Sociodemographic characteristics of patients visiting the Regional Cancer Centres in 2021.

Sociodemographic characteristics of patients visiting the Regional Cancer Centres in 2021.</p

Cancer stage by age group at the regional cancer centers, 2021.

Cancer stage by age group at the regional cancer centers, 2021.</p

Histology and HIV results availability at the regional cancer centres.

Histology and HIV results availability at the regional cancer centres.</p

Dataset.

For 50 years, comprehensive cancer treatment services were provided at one public hospital and a few private facilities in the capital city. In 2019, the services were decentralized to new national and regional centers to increase service accessibility using an integration model. This study aimed to analyze the status of the utilization of services at regional cancer centers. We analyzed data from the district health information system, focusing on patient demographics, visit type, cancer stage, and the type of treatment provided. For comparison, a trend analysis of new cancer cases recorded at the main national referral hospital between 2011–2021 was also conducted. We conducted a descriptive analysis of the variables of interest; the median was used to summarize continuous variables and percentages were used for categorical variables. A total of 29,321 patients visited the regional centers in 2021; the median age was 57 years (IQR 44–68) and 57.3% (16,815) were female. Visits to regional centres represented 38.8% (29,321/75,501) of all visits to public cancer centers; new visits accounted for 16.4% (4814/29321), and the rest were follow-up visits. Most patients (71%) had an advanced disease. The proportion of male patients with advanced-stage cancer was significantly higher than that of female patients (74% vs. 69%, P</div

Distribution of Patient Visits by Regional Cancer Centers in 2021 (n = 29,321).

Distribution of Patient Visits by Regional Cancer Centers in 2021 (n = 29,321).</p

Cancer staging distribution by cancer center.

For 50 years, comprehensive cancer treatment services were provided at one public hospital and a few private facilities in the capital city. In 2019, the services were decentralized to new national and regional centers to increase service accessibility using an integration model. This study aimed to analyze the status of the utilization of services at regional cancer centers. We analyzed data from the district health information system, focusing on patient demographics, visit type, cancer stage, and the type of treatment provided. For comparison, a trend analysis of new cancer cases recorded at the main national referral hospital between 2011–2021 was also conducted. We conducted a descriptive analysis of the variables of interest; the median was used to summarize continuous variables and percentages were used for categorical variables. A total of 29,321 patients visited the regional centers in 2021; the median age was 57 years (IQR 44–68) and 57.3% (16,815) were female. Visits to regional centres represented 38.8% (29,321/75,501) of all visits to public cancer centers; new visits accounted for 16.4% (4814/29321), and the rest were follow-up visits. Most patients (71%) had an advanced disease. The proportion of male patients with advanced-stage cancer was significantly higher than that of female patients (74% vs. 69%, P</div

Trends of new cancer patients at the main referral hospital (2011 to 2021) and the regional cancer centers (2020–2021).

Trends of new cancer patients at the main referral hospital (2011 to 2021) and the regional cancer centers (2020–2021).</p

Cancer staging by gender.

For 50 years, comprehensive cancer treatment services were provided at one public hospital and a few private facilities in the capital city. In 2019, the services were decentralized to new national and regional centers to increase service accessibility using an integration model. This study aimed to analyze the status of the utilization of services at regional cancer centers. We analyzed data from the district health information system, focusing on patient demographics, visit type, cancer stage, and the type of treatment provided. For comparison, a trend analysis of new cancer cases recorded at the main national referral hospital between 2011–2021 was also conducted. We conducted a descriptive analysis of the variables of interest; the median was used to summarize continuous variables and percentages were used for categorical variables. A total of 29,321 patients visited the regional centers in 2021; the median age was 57 years (IQR 44–68) and 57.3% (16,815) were female. Visits to regional centres represented 38.8% (29,321/75,501) of all visits to public cancer centers; new visits accounted for 16.4% (4814/29321), and the rest were follow-up visits. Most patients (71%) had an advanced disease. The proportion of male patients with advanced-stage cancer was significantly higher than that of female patients (74% vs. 69%, P</div

Attitudes towards vaccines and intention to vaccinate against COVID-19: a cross-sectional analysis - implications for public health communications in Australia

Objective To examine SARS-CoV-2 vaccine confidence, attitudes and intentions in Australian adults as part of the iCARE Study. Design and setting Cross-sectional online survey conducted when free COVID-19 vaccinations first became available in Australia in February 2021. Participants Total of 1166 Australians from general population aged 18-90 years (mean 52, SD of 19). Main outcome measures Primary outcome: responses to question € If a vaccine for COVID-19 were available today, what is the likelihood that you would get vaccinated?'. Secondary outcome: analyses of putative drivers of uptake, including vaccine confidence, socioeconomic status and sources of trust, derived from multiple survey questions. Results Seventy-eight per cent reported being likely to receive a SARS-CoV-2 vaccine. Higher SARS-CoV-2 vaccine intentions were associated with: increasing age (OR: 2.01 (95% CI 1.77 to 2.77)), being male (1.37 (95% CI 1.08 to 1.72)), residing in least disadvantaged area quintile (2.27 (95% CI 1.53 to 3.37)) and a self-perceived high risk of getting COVID-19 (1.52 (95% CI 1.08 to 2.14)). However, 72% did not believe they were at a high risk of getting COVID-19. Findings regarding vaccines in general were similar except there were no sex differences. For both the SARS-CoV-2 vaccine and vaccines in general, there were no differences in intentions to vaccinate as a function of education level, perceived income level and rurality. Knowing that the vaccine is safe and effective and that getting vaccinated will protect others, trusting the company that made it and vaccination recommended by a doctor were reported to influence a large proportion of the study cohort to uptake the SARS-CoV-2 vaccine. Seventy-eight per cent reported the intent to continue engaging in virus-protecting behaviours (mask wearing, social distancing, etc) postvaccine. Conclusions Most Australians are likely to receive a SARS-CoV-2 vaccine. Key influencing factors identified (eg, knowing vaccine is safe and effective, and doctor's recommendation to get vaccinated) can inform public health messaging to enhance vaccination rates

How well do covariates perform when adjusting for sampling bias in online COVID-19 research? Insights from multiverse analyses

COVID-19 research has relied heavily on convenience-based samples, which—though often necessary—are susceptible to important sampling biases. We begin with a theoretical overview and introduction to the dynamics that underlie sampling bias. We then empirically examine sampling bias in online COVID-19 surveys and evaluate the degree to which common statistical adjustments for demographic covariates successfully attenuate such bias. This registered study analysed responses to identical questions from three convenience and three largely representative samples (total N = 13,731) collected online in Canada within the International COVID-19 Awareness and Responses Evaluation Study (www.icarestudy.com). We compared samples on 11 behavioural and psychological outcomes (e.g., adherence to COVID-19 prevention measures, vaccine intentions) across three time points and employed multiverse-style analyses to examine how 512 combinations of demographic covariates (e.g., sex, age, education, income, ethnicity) impacted sampling discrepancies on these outcomes. Significant discrepancies emerged between samples on 73% of outcomes. Participants in the convenience samples held more positive thoughts towards and engaged in more COVID-19 prevention behaviours. Covariates attenuated sampling differences in only 55% of cases and increased differences in 45%. No covariate performed reliably well. Our results suggest that online convenience samples may display more positive dispositions towards COVID-19 prevention behaviours being studied than would samples drawn using more representative means. Adjusting results for demographic covariates frequently increased rather than decreased bias, suggesting that researchers should be cautious when interpreting adjusted findings. Using multiverse-style analyses as extended sensitivity analyses is recommended