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Study design

This study utilized PC and PHE methods, which are comparable to those used in GBD 2010 and European surveys measuring adult DWs [17, 18]. However, we differed in our survey approach and survey instruments. We used a paper version of the questionnaire, tailored to the situation, as the survey instrument instead of an electronic questionnaire. Furthermore, we conducted a face-to-face survey instead of an online survey from March 2021 to October 2022.

Research setting and participants

The primary target of the sample for this study was people who had some knowledge or awareness of a particular health state in children. Participants were preferably close contacts of children or children themselves. Due to the face-to-face nature of the survey, the large amount of human, material, and financial resources required, and the limited time available due to the epidemic control during the survey period, the respondents of this study were selected from the parents of children in the birth cohort already established by the research group; the parents of children attending and being hospitalised in Hunan Provincial Children’s Hospital, Xiangya Hospital, Xiangya No. 2 Hospital, Xiangya No. 3 Hospital; and the paediatricians of the community and the general hospitals; The general population living in other urban areas of Hunan Province and the administrative districts of Changsha, as well as university students.

The inclusion criteria for the respondents were: (1) be 18 years of age or older; (2) possess normal intelligence, a certain level of literacy, and the ability to comprehend the questionnaire; and (3) have an understanding of the content and purpose of the study, agree to participate, and voluntarily sign an informed consent form. Exclusion criteria include: (1) participants who did not meet the inclusion criteria; (2) incomplete questionnaire responses; and (3) participants who refused to cooperate or did not sign the informed consent form.

The sample size was estimated in consultation with experts in the field of statistics and computer simulations revealed that pairwise comparisons, with more than five comparisons using Probit regression analysis, were able to identify differences, and we also referred to the published literature [19], where 206 illnesses and injuries were included in face-to-face surveys of 5,750 people. The number of disease and injury categories investigated in our study was 148. The number of pairs for two-by-two comparison is 148*147/2 = 10,878, each questionnaire in this study incorporates 16 PC method pairs, a total of 10,878/16 = 680 different questionnaires are needed for one round of survey, each questionnaire needs to be completed by one person independently, 8 rounds of survey are planned in this study, the sample size to be surveyed is 8*680 = 5440, the PC method of our study investigates a total of 5455 respondents in 8 rounds of survey.

Since it was difficult for us to access high schools or middle schools to survey children under the age of 18 in person. Therefore, we surveyed a random sample of students in colleges and universities to make it more representative of the health preferences of this age group. Our survey involved health state descriptions and the anchoring tool, the PHE method, was more difficult to understand, requiring respondents to have some knowledge and equivalent measures of a certain health state, so we surveyed almost exclusively children’s parents in hospitals and neighbourhoods and required parents to have a certain level of cognition, resulting in an overall high level of education in the included population.

Determination of health statuses

After reviewing relevant literature, we identified 148 childhood health statuses based on the disease spectrum of Chinese children’s outpatient and inpatient services, the Global Burden of Childhood Diseases list, the WHO Children’s Disease Statistical List, and major childhood health statuses gained through interviews with children’s parents, as well as the disease spectrum of pediatric outpatient and inpatient services at comprehensive tertiary hospitals and children’s specialty hospitals (See Supplementary documents 1). The text describes six categories of children’s health statuses: birth defects and congenital disability diseases (24), acute infectious disease (31), chronic diseases and injuries (34), accidental injuries (36), mental and behavioral disorders diseases (14), and malignant neoplasm diseases (9). (See Supplement Table S1).

Lay description of health statuses

The principles for describing children’s health statuses are to use concise, non-clinical vocabulary, to highlight the main functional consequences and symptoms associated with the health statuses, and to keep the description to 50 words or less. The same health statuses assessed in adult DWs were identified by referring to lay descriptions of adult health statuses and incorporating them for children [20,21,22]. In the first phase of lay disease descriptions, we measured the functional and symptomatic dimensions of the 148 childhood health statuses included, using the “International Classification of Functioning, Disability, and Health, (ICF)” (https://apps.who.int/iris/bitstream/handle/10665/42407/9241545429.pdf?sequence=1) assessment scale (See Supplement Table S2). This helps to characterise the specific health statuses of these manifestations. Where possible, descriptions were determined based on standard clinical professional classification systems to accurately reflect the severity of a particular condition. The research team extensively discussed and revised the functional health and symptom presentation of the 148 childhood health statuses before finalising a preliminary version. Pediatric experts and doctors from community health service centers were consulted to review and modify the preliminary textual descriptions. This was done to ensure that they accurately reflected the characteristics, common presentations, and duration of associated symptoms involving the sequelae of impaired functioning. Finally, we obtained versions of the textual descriptions of childhood health statuses suitable for use in general population surveys using the PC and PHE methods (See Supplement Table S1).

Health status valuation

A comprehensive evaluation of the 148 children’s health statuses was conducted using the PC and PHE methodologies. The PC technique is an ordinal measure that assesses relative differences in individual functioning and health by comparing pairs of children’s health statuses. It also captures the assessor’s preferred choices for these health statuses. The PHE technique is a group health benefit transformation method that requires the assessor to retrospectively assess two hypothetical health items. The first health item is to prevent 1,000 people from developing a disease that leads to rapid death, while the second health item is to prevent 1,500, 2,000, 3,000, 5,000, or 10,000 people (based on randomly selected bids for each question) from developing a disease that is not fatal (i.e., one of the 148 health statuses for children) but would experience the symptoms and durations mentioned in the descriptions. Evaluators are asked to choose which health program they believe produces greater overall population health benefits. The PHE method is utilised for the purpose of evaluating and comparing health statuses affecting entire populations. This is achieved by estimating the propensity for children to experience a loss of welfare due to different health statuses, and subsequently translating this into an equivalent value of welfare loss.

The 148 childhood diseases were first paired two-by-two, for a total of 148*147/2 = 10,878 pairs. We arranged 16 pairs and 3 PHE questions per questionnaire, each questionnaire needed pairs without put back randomly selected from 10,878 pairs and 3 PHE questions without put back randomly selected from 148 childhood diseases until all pairs were included in the questionnaire. Each round of the survey required the completion of 10,878/16 = 680 different questionnaires (See Supplementary documents 2).

Data collection procedures and instruments

In this study, a paper-based questionnaire served as the primary survey instrument. The questionnaire was developed by the research team in consultation with experts and was finalized based on the pre-survey results. The questionnaire mainly consisted of basic socio-demographic information of the respondents (e.g., age, gender, usual address, type of household registration, marital status, education level, annual household income level, type of occupation, presence of children in the household, presence of medical background, etc.), as well as 16 PC questions and 3 PHE questions.

Trained investigators conducted face-to-face interviews. Respondents were informed of the survey’s purpose and questionnaire content and asked to sign an informed consent form. The enumerator supervised respondents while they completed the questionnaire and answered any questions. The questionnaire was completed in full. Respondents did not receive payment for their participation in the survey. The investigator prompted respondents to imagine two children with the disease and to weigh who was healthier between the two. Two investigators worked in pairs to enter the completed questionnaires into a pre-developed computer program.

Statistical analysis

The statistical analyses for this study were conducted using R software (version 4.3.1), Stata MP software (version 17.0), and Microsoft Excel 2021. Probit regression models were used to estimate relative outcomes for childhood health statuses based on pooled PC data. The result reflects the relative differences in severity between childhood health statuses on a quantitative scale, and also shows participants’ choice preferences for each childhood health statuses [23]. The probit regression model was used to determine the selection of the first disease and injure as the healthier state in pairwise comparisons. A response variable of 1 was assigned to this selection, while a value of 0 was assigned to the alternative selection. The probit regression model incorporated indicator variables for each disease and injure, with a value of 1 assigned to the first state in a PC, −1 to the second state in a PC, and 0 to all states other than the pair being considered. Additionally, the interval regression model was used to analyze the pooled group health equivalence data. Finally, a linear regression model was used to anchor the estimates from the probit regression. Logit transformations based on the PHE responses were performed to map to a DW scale of 0 to 1. Finally, 1000 bootstrap iterations were used to calculate 95% uncertainty intervals (UIs) [17, 18].

In addition, we conducted a trend analysis of the DWs of the childhood health statuses included in this study, to validate the logical soundness of the study and the reliability of the DW values. Trend analysis i.e. by plotting a trend line on the DW values of diseases with severity ratings to see if they logically fit the intuition. R_{Spearman’s correlation coefficient} (rs) was used to test for correlations between the DW values obtained for different subgroups and to identify overall differences in DW values measured under different population characteristics. These factors included gender (male and female), age (≥ 35 and < 35 years), education level (bachelor’s degree/above and below), annual household income (high income ≥ 100,000 yuan and low income < 100,000 yuan), type of household registration (urban and rural), presence of a medical background, and physical labor status (manual and non-manual), whether there are children in the family (with child and without child), and whether there is a medical background (with medical background (MB) and without MB). Using the results of the ICF assessment of 148 children’s health statuses (See Supplement Table S3), we investigated the impact of various functional attributes and symptomatic manifestations on children’s DW values.

US senators Josh Hawley and Richard Blumenthal have asked the heads of the Federal Trade Commission (FTC) and the Securities and Exchange Commission (SEC) to investigate revenue from ads on Facebook and Instagram that promote scams and banned goods.

“The FTC and SEC should immediately open investigations and, if the reporting is accurate, pursue vigorous enforcement action where appropriate” to force Meta to disgorge profits, pay penalties and agree to cease running such advertisements, Hawley and Blumenthal wrote in a letter to the federal agencies.

Earlier this month, Reuters reported that internal documents from late 2024 stated that that year – about $16bn – from illicit advertising. One document noted Meta, which owns Facebook and Instagram, earns $3.5bn in revenue from “higher risk” scam ads every six months. Other documents stated that Meta’s anti-fraud rules didn’t appear to apply to many ads that regulators and the company’s own staff believed “violated the spirit” of its rules against scam advertising.

In response to the Reuters report, Meta said it had reduced user reports of scams by 58% over the last 18 months.

The Hawley-Blumenthal letter “makes claims that are exaggerated and wrong”, Meta spokesman Andy Stone said. “We aggressively fight fraud and scams because people on our platforms don’t want this content, legitimate advertisers don’t want it and we don’t want it either.”

Hawley, a Republican, and Blumenthal, a Democrat, expressed skepticism about Meta’s efforts to combat illicit advertising. They pointed to the company’s “ad library”, a publicly accessible database of advertising that appears on Meta’s social-media platforms.

“Even a short review of Meta’s Ad Library at the time of this letter shows clearly identifiable advertisements for illicit gambling, payment scams, crypto scams, AI deepfake sex services, and fake offers of federal benefits,” they wrote.

The senators cited Reuters reporting that Meta itself estimated its platforms were involved in a third of all scams in the US, and went on to note that the FTC estimates Americans lost $158.3bn to scams last year.

“Scams have been allowed to take over Facebook and Instagram as Meta has drastically cut its safety staff, including for FTC mandated reviews, even as it dumps unimaginable sums into its generative AI projects.“

Blumenthal and Hawley expressed particular concern about fake ads purporting to represent the US government or political figures. They cited an example of a bogus ad that claimed Donald Trump was offering $1,000 to recipients of food assistance.

“While Meta has been warned about advertisement deepfakes impersonating politicians, it still continues to run fraudulent clips,” their letter states. “The beneficiaries of these scams are often cybercrime groups based in China, Sri Lanka, Vietnam and the Philippines.”