Estimating socioeconomic status for health equity surveillance in Cameroon: an expert opinion survey

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An exception is the Demographic and Health Surveys (DHS)’s wealth quintile index constructed from household ownership assets. To facilitate health equity surveillance, method of individual SES estimation requiring fewer number of household assets is needed. The objective of this study was to identify the DHS assets most relevant for measuring SES in Cameroon. Methods Participants interviewed with a structured questionnaire included stakeholders involved in the design and implementation of DHS in Cameroon for many years. Using a 5-point Likert scale, experts graded DHS assets’ likelihood to measure SES. The questionnaire was strongly reliable (Cronbach’s alpha: 0.943, 95% CI: 0.920–0.961, p < 0.001) for using the 29 items retained to measure SES. Results The probabilities of agreeing that an asset can be a useful measure of SES varied from 0.016 to 0.047. The 12 DHS assets most likely to measure SES included having Refrigerator (85.3%), Television (83.8%), Laptop (79.4%), Mixer (77.9%), Computer (77.9%), Agricultural land (77.9%), Cable/Satellite (76.5%), Cell phone (76.5%), Modem/Internet key (73.5%), Water pump (72.1%), Car/truck (72.1%) and Gas stove (72.1%) with a respective probability (prior) of 0.047, 0.046, 0.044, 0.043, 0.043, 0.043, 0.042, 0.042, 0.041, 0.040, 0.040 and 0.040. Conclusions This research underscores the importance of integrating local expert insights to refine the measurement of SES, promoting improved health outcomes in populations, particularly in Cameroon. Future research should explore the application of this expert-opinion-driven framework in various contexts to create more comprehensive, robust, and reliable SES indicators. Prior Probability Expert Opinion Demographics and Health Survey Socioeconomic Status Health equity Figures Figure 1 Background Despite increasing awareness of socioeconomic status’s (SES) association with health outcomes, there is no widely accepted and rapidly implementable estimation of SES measures in resource-limited settings. Health inequities are particularly unmasked by acute conditions, such as trauma, where low SES is both a risk factor for injury and associated with poor outcomes. The Demographic and Health Surveys (DHS)’s wealth quintile index is constructed from household ownership assets, limiting feasibility of data collection in time-sensitive clinical conditions, such as trauma. To facilitate health equity surveillance in acute care settings, method of individual SES estimation requiring fewer number of household assets is needed. Since the 1980s, nations around the world have been increasingly called upon to address health inequities, which are systematic, unfair and avoidable differences in health outcomes and their determinants between segments of the population, such as SES, demography or geography [ 1 – 3 ]. Health equity represents the absence of systematic health disparities between groups with different economic status [ 1 ]. Due to the lack of equity in the organization of societies, opportunities to thrive and be healthy are not distributed equally between and within societies. For example, people in low-income countries are much more likely to die or become disabled as a result of trauma because of the high cost of treatment, which is often surgical [ 4 , 5 ]. Evaluation of public health equity is important to ensure that interventions do not increase health inequalities and that they meet the stated objectives of interventions to reduce health inequities. Monitoring health inequities and measuring the impact of interventions have been identified by the world health organization (WHO) as key strategies needed to address disparities [ 6 ]. This monitoring involves knowing the socio-economic status of users requiring healthcare. There is as yet no widely accepted and rapidly implementable measure of SES to address this urgent need. The lack of population-specific, validated and easily implemented SES measures that can be applied in a variety of contexts is a major obstacle to addressing equity issues in resource-limited settings. The method of measurement used is that provided by the WHO which allows existing datasets to be explored and individual researchers' data to be downloaded for analysis, with the data being derived from the DHS to establish the wealth quintile [ 7 ]. A number of tools, still incorporating data from the DHS, have been designed to improve the tool proposed by the WHO [ 8 ]. However, these measures already in use have limitations as they do not include the collection of data to measure wealth at the individual level and therefore do not solve the problem of inequality as they are not precision techniques for monitoring SES through primary data collection in the field ( Inequality - Income Inequality - OECD Data ). In Cameroon and other countries, factors such as education, occupation, income, and some of the 29 DHS indicators are used to characterize the SES of an individual [ 9 ]. Factors such as income, although a reliable reflection of economic status, can be difficult to assess in low-income countries where employment is generally irregular [ 10 ]. Assessing consumption and expenditure is even more time consuming when we are in an acute care setting where time and patient tolerance are limited. The lack of quick and easy-to-implement tools to accurately measure SES in acute care settings remains and is a significant barrier to monitoring and mitigating health disparities in trauma and other time-sensitive conditions. The determination of the patient's socio-economic level by the health care staff is based on the collection of the 29 indicators used in the measurement of the socio-economic level to obtain a classification of the patient's level according to the welfare quintile, which is not very obvious, because the management must be done within a short time to improve the chances of follow-up of the patient. It is difficult, when faced with any illness of patients arriving in hospital, to assess the patient's well-being quintile by filling in the 29 indicators used by the DHS [ 11 , 12 ]. Completing the 29 indicators to measure the level of well-being takes more time and therefore increases the risk of negative health consequences for the patient received in hospital. In this case it is more difficult to determine their level of well-being and to ascertain whether they have certain advantages or not. Variables methods, including statistical techniques and mathematical methods, exist for reducing the number of variables. For instance, there is the economic cluster, which is an analysis-based algorithm, to reduce the number of variables by selecting 4 or 5 between 29 metrics of DHS [ 13 , 14 ] This algorithm is based on a simple population-specific metrics of economic status using nationally representative DHS household items data [ 15 ]. This technique relies exclusively on statistical methods and has not yet been validated by experts in the field of survey data collection and analysis in Cameroon. To ensure complementarity, we propose capturing, in parallel, the main variables to be retained according to expert opinions in the field. The objective of this study was is to propose an alternative to statistical and mathematical models based on expert opinion for selecting DHS variables used to measure SES and to obtain prior probabilities for each selected DHS item. Methods Study design Study period and setting This study used a cross-sectional design to assess expert opinion regarding the indicators used to measure SES in an in-person interview. Expert opinion surveys are useful for creating rankings when more objective data are not available. Data was collected between December 2023 to April 2024 in Yaoundé (the capital of Cameroon) targeting experts from institutions and organizations that have been involved in the design and implementation of DHS (or any other aspect) in Cameroon for many years. Study population and sampling All experts from diverse background identified from a comprehensive list from the Cameroon National Institute of Statistics were included in the study. Data collection procedure Experts were identified and invited to participate in the study. An Open Data Kit (ODK) was used to administer the questionnaire. Variables included, in addition to the 29 DHS asset items, sociodemographic information such as age, sex, marital status, basic training received, level of education, year of professional experience, type of institution, researcher , and grade while participant exposure to DHS included access to DHS data, training on DHS, involvement in the design of the DHS , and involvement in the analyzes DHS data. Data analysis The first step was to present the socio-demographic characteristics of the experts surveyed. This was done using absolute and relative frequencies. The reliability of the questionnaire was assessed using the Cronbach's alpha coefficient (with 95% confidence interval (CI)) to ensure consistency among DHS asset items. Using a 5-point Likert scale (strongly agree, agree, neither agree nor disagree, disagree or strongly disagree), experts graded DHS household assets’ likelihood to measure SES. The 29 asset items were grouped into a binary variable where 1 corresponds to agree or strongly agree, and 0 otherwise. The prior probability for each item was calculated as the proportion of participants who agreed or strongly agreed for the corresponding item. Data were collected, entered into ODK and analysed with R software version 4.3.0. Results Table 1 presents the Socio-demographic characteristics of the experts’ interview. We included a total of 67 experts amongst which, 50 (74.6%) males and 17 (25.4) females. Regarding the marital status, 47% of the experts were married and 47% lived alone. Education levels in the sample show a significant concentration in the fields of Statistics and Demography, with 35 participants (52.2%) and 21 participants (31.3%) respectively. Other fields, such as Economics, Medicine, and Nutrition, were less represented. Work experience varied among participants, with notable representation in all ranges. The largest proportion, 31.8% of participants, had between 8 and 30 years of experience, while 27.3% had between 6 and 8 years. Table 1 Distribution of sociodemographic information of experts Variable Frequency n (%) Gender Female 17 (25.4) Male 50 (74.6) Age mean (sd) 33.7 (5.7) Age (years) 25–29 14 (23.0) 30–34 26 (42.6) 35–39 14 (23.0) 40–59 7 (11.4) Marital status Single 31 (47.0) Cohabiting 3 (4.5) Maried 31 (47.0) Widowed 1 (1.5) Education Demographer 21 (31.3) Economist 3 (4.5) Mathematics 1 (1.5) Nutrition 1 (1.5) Reproductive health 1 (1.5) Public health 2 (3.0) Social Sciences 1 (1.5) Statistics 35 (52.2) Medicine 2 (3.0) Number of years of experience 1–3 16 (24.2) 4–5 11 (16.7) 6–7 18 (27.3) 8 or more 21 (31.8) Reliability of the questionnaires The internal consistency in the 29 item retained in the survey was excellent with Cronbach's alpha is 0.943 (95 CI: 0.920–0.961) Participant exposure to DHS All the experts expressed their familiarity with the Demographic and Health Surveys (Fig. 1 ). Similarly, 73% of experts stated that they used DHS data. However, they are less involved in the design and implementation of these surveys. Regarding the variables used in the DHS to measure socio-economic status, 38% of the respondents indicated that they were familiar with them. Level of expert opinion on the 29 variables to measure economic status Table 2 presents the expert opinion on the 29 variables identified to measure household economic status. Responses on a scale of 1 to 5 (1 = strongly disagree; 2 = disagree; 3 = neither agree nor disagree; 4 = Agree; 5 = strongly agree). Expert opinions varied from item to item and from grade to grade. For instance, television and cell phone ownership received high levels of agreement, with 52.3% and 43.1% of participants, respectively, indicating strong agreement. Conversely, certain items like the motorboat showed a more divided opinion with only 26.2% strongly agreeing about its contribution to SES. Ownership of household appliances, such as the refrigerator and stove, also showed a significant level of positive response, with 43.1% and 36.9% of respondents agreeing or strongly agreeing on their importance, respectively. In contrast, lower agreement levels for items like CD/DVD players (only 18.5% agreeing) indicate that such items may not carry as much economic significance. Regarding transportation items, car/truck ownership showed impressive support, with 38.5% agreeing and 33.8% strongly agreeing. In contrast, the animal-drawn cart received more mixed opinions. Computer tablet and laptop/notebook also received substantial positive responses, computer tablet attracted the highest agreement (47.7%). Table 2 Distribution of selection of economic variables and expert opinion Item Strongly disagree n (%) Disagree n (%) Neither Agree Nor disagree n (%) Agree n (%) Strongly agree n (%) Radio 6 (9.2) 14 (21.5) 11 (16.9) 25 (38.5) 9 (13.8) Television 3 (4.6) 2 (3.1) 6 (9.2) 34 (52.3) 20 (30.8) Home phone 12 (18.5) 10 (15.4) 9 (13.8) 18 (27.7) 16 (24.6) Desktop computer 6 (9.2) 8 (12.3) 9 (13.8) 22 (33.8) 20 (30.8) Refrigerator 3 (4.6) 3 (4.6) 4 (6.2) 28 (43.1) 27 (41.5) Stove 3 (4.6) 6 (9.2) 11 (16.9) 21 (32.3) 24 (36.9) Gaz stove 1 (1.5) 6 (9.2) 11 (16.9) 26 (40.0) 21 (32.3) Air conditioner 6 (9.2) 9 (13.8) 11 (16.9) 16 (24.6) 23 (35.4) Fan 1 (1.5) 9 (13.8) 14 (21.5) 26 (40.0) 15 (23.1) CD/DVD player 9 (13.8) 21 (32.3) 16 (24.6) 12 (18.5) 7 (10.8) Grinding mill 6 (9.2) 15 (23.1) 16 (24.6) 24 (36.9) 4 (6.2) Mixer/Grinder 2 (3.1) 4 (6.2) 8 (12.3) 33 (50.8) 18 (27.7) Modem/Internet key 2 (3.1) 6 (9.2) 8 (12.3) 26 (40.0) 23 (35.4) Cable/Satellite 2 (3.1) 4 (6.2) 8 (12.3) 28 (43.1) 23 (35.4) Generator 8 (12.3) 3 (4.6) 13 (20.0) 17 (26.2) 24 (36.9) Solar panel 6 (9.2) 3 (4.6) 14 (21.5) 16 (24.6) 26 (40.0) Water pump 3 (4.6) 3 (4.6) 13 (20.0) 17 (26.2) 29 (44.6) Clock/pendulum 7 (10.8) 13 (20.0) 16 (24.6) 23 (35.4) 6 (9.2) Watch 6 (9.2) 16 (24.6) 13 (20.0) 23 (35.4) 7 (10.8) Cell phone 3 (4.6) 5 (7.7) 7 (10.8) 22 (33.8) 28 (43.1) Bicycle 6 (9.2) 14 (21.5) 21 (32.3) 15 (23.1) 9 (13.8) Motocycle/scooter 3 (4.6) 10 (15.4) 18 (27.7) 19 (29.2) 15 (23.1) Animal-drawn cart 11 (16.9) 10 (15.4) 20 (30.8) 13 (20.0) 11 (16.9) Car/truck 6 (9.2) 3 (4.6) 9 (13.8) 25 (38.5) 22 (33.8) Motorboat 13 (20.0) 12 (18.5) 10 (15.4) 13 (20.0) 17 (26.2) Laptop/notebook 1 (1.5) 6 (9.2) 6 (9.2) 28 (43.1) 24 (36.9) Computer tablet 3 (4.6) 6 (9.2) 8 (12.3) 31 (47.7) 17 (26.2) Agricultural land 2 (3.1) 4 (6.2) 7 (10.8) 27 (41.5) 25 (38.5) Farm animals 4 (6.2) 10 (15.4) 12 (18.5) 23 (35.4) 16 (24.6) Socio economic variables according to experts' preferences We have grouped each item into a binary variable where 1 corresponds to agree or strongly agree responses, and 0 corresponds to the other modalities. Responses were quantified in terms of frequency in descending order and presented in Table 3 . Furthermore, the probabilities of saying "yes" to the various items were also presented in the same table. The probabilities of validating an item as a measure economic of level varied from 0.016 to 0.047. Thus, the variables most likely to measure economic status include refrigerator (0.047), television (0.046), laptop (0.044), mixer (0.043), computer tablet (0.043), agricultural land (0.043), cable (0.042), cell phone (0.042), modem (0.041), water pump (0.041), and car (Table 3 ). Table 3 Distribution of economic variables according to experts' preferences and probability of selection of each item Item n (%) Probability Refrigerator 58 (85.3) 0.047 Television 57 (83.8) 0.046 Laptop/notebook 54 (79.4) 0.044 Mixer/Grinder 53 (77.9) 0.043 Computer tablet 53 (77.9) 0.043 Agricultural land 53 (77.9) 0.043 Cable/Satellite 52 (76.5) 0.042 Cell phone 52 (76.5) 0.042 Modem/Internet key 50 (73.5) 0.041 Water pump 49 (72.1) 0.040 Car/truck 49 (72.1) 0.040 Gas stove 49 (72.1) 0.040 Stove 48 (70.6) 0.039 Desktop computer 45 (66.2) 0.037 Solar panel 44 (64.7) 0.036 Fan 43 (64.2) 0.035 Generator 43 (63.2) 0.035 Air conditioner 41 (60.3) 0.033 Farm animals 41 (60.3) 0.033 Radio 36 (52.9) 0.029 Home phone 36 (52.9) 0.029 Motocycle/scooter 34 (50.0) 0.028 Motorboat 32 (47.1) 0.026 Clock/pendulum 31 (45.6) 0.025 Watch 31 (45.6) 0.025 Grinding mill 29 (42.6) 0.024 Bicycle 24 (35.8) 0.019 Animal-drawn cart 24 (35.3) 0.019 CD/DVD player 20 (29.4) 0.016 Discussion SES is a combination of factors that affect the health condition of an individual or a family. These factors may include education, income, and employment type [ 16 ]. Measuring SES level through indicators of ownership of goods is a common approach in public health and social research. The DHS are widely used survey tool that include items related to ownership of goods to assess household wealth and SES. These items are included in the DHS because of they may directly influences health outcome [ 17 ]. The most frequent approach to constructing an item index from a set of variables is a statistical method known as Principal Components Analysis (PCA). PCA is a technique for data reduction; it utilizes the correlations among indicators to create a series of uncorrelated principal component [ 15 , 18 , 24 ]. The objective of this study was is to propose an alternative to statistical and mathematical models based on expert opinion for selecting DHS variables used to measure SES and to obtain prior probabilities for each selected DHS item. The selection of ownership goods indicators can provide valuable insights into the living standards of households. Our findings indicate that the experts identified ownership of refrigerator, television, laptop/notebook, and mixer/grinder and computer tablet as the most important indicators in measuring socioeconomic status. These results show a clear consensus regarding certain items, such as refrigerators and televisions, which were strongly agreed by the experts as critical indicators of SES. The overpowering agreement on these items highlights their potential perceived importance in creating a stable household environment, as they are often associated with basic needs and comfort. In contrast, items such as motorboats and CD/DVD players received lower levels of agreement, suggesting that the relevance of certain items may be context-dependent and less associated with current socio-economic paradigms. When looking at the probabilities assigned to various items to measure SES, items such as refrigerators and televisions emerged as the most strongly supported items, indicating their integral role in determining SES. The quantitative assessment offers a compelling argument for prioritising specific items in future versions of the DHS. By focusing on high-probability measures, policymakers and researchers can ensure that the most relevant indicators are incorporated into assessments of socio-economic well-being, thereby improving the reliability and validity of the conclusions drawn. In addition, the observation that technological items such as laptops and tablet computers feature prominently among the selected variables reflects a wider socio-economic trend towards digital inclusion. Access to technology is increasingly seen as a determinant of economic status, influencing opportunities for education, employment and social mobility. The implications of this shift suggest that measures of socio-economic status must continually evolve, incorporating emerging item classes that respond to changing economic landscapes. Other studies have suggested similar items to estimate socio-economic status and inequality within populations [ 18 – 20 ]. A related study employed the Delphi method to develop shortened wealth indices using survey data from 16 countries, for Cameroon, 9 items were identified of which, 6 items (Television, Cable/Satellite, Refrigerator, Fan Mixer/Grinder, Watch) were related to items [ 8 ]. Similarly, a study conducted in Iran concluded that 6 items out of 33 in a simple item index, the items comprised of kitchen, bathroom, vacuum cleaner, washing machine, freezer, and personal computer [ 21 ], unlike our study that used expert opinions, this research employed statistical algorithm in order to reach a shortened list. Furthermore, According to the Delphi evaluation by expert consensus on Iran, 15 items were identified of which 7 (house ownership, ,personal computer/laptop, smart cell phone, 3D TV, dishwasher, microwave, and car ownership.) were related to items [ 22 ]. Comparisons of results between countries are challenging due to the difference in indicators selection, constructing approaches and items sets. To our knowledge, no study has assessed an alternative to statistical and mathematical models based on expert opinion for selecting DHS variables for measuring SES in Cameroon. Our study involved expert in diverse background implicated directly in the DHS, thus ensuring that the validated composite items developed in this study are generalizable to national-level data and can be applied to population subgroups. Many studies highlighted some limits in suing the PCA approach, One notable limitation is the ‘urban bias’, as it is based on items that better reflect social stratification in urban areas than in rural areas. For example urban households are much more likely to have access to electricity compared to the rural households, while items that rural households may have, such as access to land and livestock, are less often considered [ 17 ]. Furthermore, a study conducted by Houweling et al. indicated that while PCA can provide a convenient way of constructing composite indices, it may yield odd results when applied to short lists of items [ 23 ]. Expert opinion is essential when selecting indicators for socio-economic measurement. When selecting indicators, experts take into account factors such as cultural relevance, local context, and survey feasibility. Nevertheless, relying on expert opinion alone could potentially introduce biases based on subjective judgment, personal experiences, or limited perspectives. In this context, experts can provide prior beliefs on the relationship between ownership indicators and socio-economic status. These prior beliefs can be formalized as Bayesian priors and combined with observed data on ownership indicators to update and refine the estimation of socio-economic levels. Despite the fact that the expert opinion approach is an innovative method of measuring SES, the underlying subjectivity of expert opinion can be biased, so it is important to find a balance between expert opinion and empirical evidence. In support of this, our results recommend the development of a Bayesian model that integrates expert opinion and empirical evidence, which will improve the robustness of SES measurement by providing a more detailed insight into the socio-economic context. Limitations Although the study provides a valuable contribution to the field, it is important to recognize its limits. Like other opinion survey studies, our study relies on judgement and perception. Our study is still reliable as the potential selection bias was reduced by obtaining a representative list of experts from the Cameroon National Institute of Statistics; and the internal consistency of the questionnaire was excellent. Conclusion This study presents a new approach to the selection of variables from the DHS to measure SES, highlighting the importance of expert opinion. We were able to identify key indicators in particular the ownership of items such as fridges, televisions, laptops and other household appliances deemed essential for accurately assessing the economic situation of populations, particularly in the context of Cameroon. This research underscores the importance of integrating local expert insights to refine the measurement of SES, promoting improved health outcomes in populations, particularly in Cameroon. Future research should explore the application of this expert-opinion-driven framework in various contexts to create more comprehensive, robust, and reliable SES indicators Abbreviations CI Confidence Interval DHS Demographic and Health Surveys ODK Open Data Kit OECD Organization for Economic Cooperation and Development PCA Principal Components Analysis SES Socioeconomic Status WHO World Health Organization Declarations Ethics approval The study obtained an ethical approval number (00/57/CRERSHC/2023) from the Regional Ethics Committee of Yaoundé, and was performed in accordance with the Declaration of Helsinki. Prior to the administration of each questionnaire, the surveyor fully explained to each expert on the basis of an information sheet the objectives and procedures of the study, and the verbal and written consent of those willing to participate was collected. Data anonymity and confidentiality were respected throughout the research process. Informed consent Informed consent from all participants included in this study was obtained Availability of data and materials The data generated from this study are available on reasonable request. Competing interest The authors declare no conflict of interest in this study Funding The study did not receive any funding from agency in the public and commercial sector. Authors' contributions GNT, ACM, CJ, CNB, and AH conceptualized the study. YNE, AN, JSEK, and MNN wrote the method and collected the data. GPLD, BBT, and CNB analyzed and interpreted the data. CNB, YNE, and MNN prepared the draft. 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On weighting the components of the human development index: a statistical justification. J Hum Dev Capabilities. 2011;12(2):183–202. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5603503","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":393651048,"identity":"3f24f1d7-11ba-461c-80f5-8ef96ac657ce","order_by":0,"name":"Collins NkumBuh","email":"","orcid":"","institution":"Meilleur Accès aux Soins de Santé (M.A. Santé)","correspondingAuthor":false,"prefix":"","firstName":"Collins","middleName":"","lastName":"NkumBuh","suffix":""},{"id":393651049,"identity":"899a27f2-2c56-453a-9a20-3bbeb1c1b80a","order_by":1,"name":"Yannick Niamsi Emalio","email":"","orcid":"","institution":"Higher Institute for Scientific and Medical Research","correspondingAuthor":false,"prefix":"","firstName":"Yannick","middleName":"Niamsi","lastName":"Emalio","suffix":""},{"id":393651050,"identity":"3d93c79e-84cb-4f18-9628-50821ede14d8","order_by":2,"name":"Marie Nicole Ngoufack","email":"","orcid":"","institution":"Challenges Initiative Solutions","correspondingAuthor":false,"prefix":"","firstName":"Marie","middleName":"Nicole","lastName":"Ngoufack","suffix":""},{"id":393651051,"identity":"7fc2c053-ad10-4cd0-a513-5aad296134c8","order_by":3,"name":"Gilles Protais Lekelem Dongmo","email":"","orcid":"","institution":"Universiteit Hasselt","correspondingAuthor":false,"prefix":"","firstName":"Gilles","middleName":"Protais Lekelem","lastName":"Dongmo","suffix":""},{"id":393651052,"identity":"ac6465b2-aa6e-47a2-8fec-6ba689f9548d","order_by":4,"name":"Brian Bongwong Tamfon","email":"","orcid":"","institution":"Ministry of Public Health","correspondingAuthor":false,"prefix":"","firstName":"Brian","middleName":"Bongwong","lastName":"Tamfon","suffix":""},{"id":393651053,"identity":"754b0d60-83dd-40ad-aed1-0d2ecd8a04b8","order_by":5,"name":"Aude Nanfak","email":"","orcid":"","institution":"Challenges Initiative Solutions","correspondingAuthor":false,"prefix":"","firstName":"Aude","middleName":"","lastName":"Nanfak","suffix":""},{"id":393651054,"identity":"5915197b-b7e6-4182-bd11-7bced2e66d5b","order_by":6,"name":"Jerome Sedowo Eyi Kodjo","email":"","orcid":"","institution":"Challenges Initiative Solutions","correspondingAuthor":false,"prefix":"","firstName":"Jerome","middleName":"Sedowo Eyi","lastName":"Kodjo","suffix":""},{"id":393651055,"identity":"3a8eb74e-5349-4032-909a-aadb031e634d","order_by":7,"name":"Alain Chichom-Mefire","email":"","orcid":"","institution":"University of Buea","correspondingAuthor":false,"prefix":"","firstName":"Alain","middleName":"","lastName":"Chichom-Mefire","suffix":""},{"id":393651056,"identity":"0fbe07a4-5d7d-4380-96c5-9dc85e65ab32","order_by":8,"name":"Catherine Juillard","email":"","orcid":"","institution":"University of California Los Angeles","correspondingAuthor":false,"prefix":"","firstName":"Catherine","middleName":"","lastName":"Juillard","suffix":""},{"id":393651058,"identity":"21f35522-2317-469f-8f20-44276219118f","order_by":9,"name":"Alan Hubbard","email":"","orcid":"","institution":"University of California Berkeley","correspondingAuthor":false,"prefix":"","firstName":"Alan","middleName":"","lastName":"Hubbard","suffix":""},{"id":393651059,"identity":"bad2f174-b25a-45d4-aa3d-fcee144ad556","order_by":10,"name":"Georges Nguefack-Tsague","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCklEQVRIiWNgGAWjYLCCCgYGAyjTBogZGw8Q1HIGqIUHwkwDaWkgScthMIlXizn/GsMHB2rsjO35Dz9+zVNx3m5t+2GgLTU20bi0WM54Y2xw4FiyGY9Empk1z5nbydvOJAK1HEvLbcChxeDGGTPpD2wHbHgkGMyMedtuJ5sdAGphbDiMT4v5jwP/gFr4j38z5v13Ltns/EMCWs73mDEcbDtgxsOQY/yYt+GAndkNgrawFUsc7Es25rmRU8Y451hygtkNoC0J+Pxy/vDGDwe+2Rm29x/f/OFNjZ292fn0hw8+1Njg1MIgkQBnskkAiUSwygSsaqGA/wCcyfwBSNjjUzwKRsEoGAUjEwAA/KFof0I574QAAAAASUVORK5CYII=","orcid":"","institution":"University of Yaoundé 1","correspondingAuthor":true,"prefix":"","firstName":"Georges","middleName":"","lastName":"Nguefack-Tsague","suffix":""}],"badges":[],"createdAt":"2024-12-08 14:53:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5603503/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5603503/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":75307986,"identity":"487e3c6a-c32b-4020-be51-1358ac755396","added_by":"auto","created_at":"2025-02-03 08:41:34","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":10356,"visible":true,"origin":"","legend":"\u003cp\u003eParticipants’ exposure to DHS\u003c/p\u003e","description":"","filename":"Onlinedrawingimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5603503/v1/08af2613bd4fccd2fa37b016.png"},{"id":75962366,"identity":"255b89b9-bac0-4ff5-ad89-0f01f7db9672","added_by":"auto","created_at":"2025-02-11 03:08:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":947133,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5603503/v1/9ed8467e-5ce6-44ed-b252-c4ce3d870e25.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Estimating socioeconomic status for health equity surveillance in Cameroon: an expert opinion survey","fulltext":[{"header":"Background","content":"\u003cp\u003eDespite increasing awareness of socioeconomic status\u0026rsquo;s (SES) association with health outcomes, there is no widely accepted and rapidly implementable estimation of SES measures in resource-limited settings. Health inequities are particularly unmasked by acute conditions, such as trauma, where low SES is both a risk factor for injury and associated with poor outcomes. The Demographic and Health Surveys (DHS)\u0026rsquo;s wealth quintile index is constructed from household ownership assets, limiting feasibility of data collection in time-sensitive clinical conditions, such as trauma. To facilitate health equity surveillance in acute care settings, method of individual SES estimation requiring fewer number of household assets is needed. Since the 1980s, nations around the world have been increasingly called upon to address health inequities, which are systematic, unfair and avoidable differences in health outcomes and their determinants between segments of the population, such as SES, demography or geography [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Health equity represents the absence of systematic health disparities between groups with different economic status [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Due to the lack of equity in the organization of societies, opportunities to thrive and be healthy are not distributed equally between and within societies. For example, people in low-income countries are much more likely to die or become disabled as a result of trauma because of the high cost of treatment, which is often surgical [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Evaluation of public health equity is important to ensure that interventions do not increase health inequalities and that they meet the stated objectives of interventions to reduce health inequities. Monitoring health inequities and measuring the impact of interventions have been identified by the world health organization (WHO) as key strategies needed to address disparities [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. This monitoring involves knowing the socio-economic status of users requiring healthcare. There is as yet no widely accepted and rapidly implementable measure of SES to address this urgent need. The lack of population-specific, validated and easily implemented SES measures that can be applied in a variety of contexts is a major obstacle to addressing equity issues in resource-limited settings. The method of measurement used is that provided by the WHO which allows existing datasets to be explored and individual researchers' data to be downloaded for analysis, with the data being derived from the DHS to establish the wealth quintile [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. A number of tools, still incorporating data from the DHS, have been designed to improve the tool proposed by the WHO [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. However, these measures already in use have limitations as they do not include the collection of data to measure wealth at the individual level and therefore do not solve the problem of inequality as they are not precision techniques for monitoring SES through primary data collection in the field (\u003cem\u003eInequality - Income Inequality - OECD Data\u003c/em\u003e). In Cameroon and other countries, factors such as education, occupation, income, and some of the 29 DHS indicators are used to characterize the SES of an individual [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Factors such as income, although a reliable reflection of economic status, can be difficult to assess in low-income countries where employment is generally irregular [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Assessing consumption and expenditure is even more time consuming when we are in an acute care setting where time and patient tolerance are limited. The lack of quick and easy-to-implement tools to accurately measure SES in acute care settings remains and is a significant barrier to monitoring and mitigating health disparities in trauma and other time-sensitive conditions.\u003c/p\u003e \u003cp\u003eThe determination of the patient's socio-economic level by the health care staff is based on the collection of the 29 indicators used in the measurement of the socio-economic level to obtain a classification of the patient's level according to the welfare quintile, which is not very obvious, because the management must be done within a short time to improve the chances of follow-up of the patient. It is difficult, when faced with any illness of patients arriving in hospital, to assess the patient's well-being quintile by filling in the 29 indicators used by the DHS [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Completing the 29 indicators to measure the level of well-being takes more time and therefore increases the risk of negative health consequences for the patient received in hospital. In this case it is more difficult to determine their level of well-being and to ascertain whether they have certain advantages or not.\u003c/p\u003e \u003cp\u003eVariables methods, including statistical techniques and mathematical methods, exist for reducing the number of variables. For instance, there is the economic cluster, which is an analysis-based algorithm, to reduce the number of variables by selecting 4 or 5 between 29 metrics of DHS [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] This algorithm is based on a simple population-specific metrics of economic status using nationally representative DHS household items data [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. This technique relies exclusively on statistical methods and has not yet been validated by experts in the field of survey data collection and analysis in Cameroon. To ensure complementarity, we propose capturing, in parallel, the main variables to be retained according to expert opinions in the field. The objective of this study was is to propose an alternative to statistical and mathematical models based on expert opinion for selecting DHS variables used to measure SES and to obtain prior probabilities for each selected DHS item.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design Study period and setting\u003c/h2\u003e \u003cp\u003eThis study used a cross-sectional design to assess expert opinion regarding the indicators used to measure SES in an in-person interview. Expert opinion surveys are useful for creating rankings when more objective data are not available. Data was collected between December 2023 to April 2024 in Yaound\u0026eacute; (the capital of Cameroon) targeting experts from institutions and organizations that have been involved in the design and implementation of DHS (or any other aspect) in Cameroon for many years.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy population and sampling\u003c/h3\u003e\n\u003cp\u003eAll experts from diverse background identified from a comprehensive list from the Cameroon National Institute of Statistics were included in the study.\u003c/p\u003e\n\u003ch3\u003eData collection procedure\u003c/h3\u003e\n\u003cp\u003eExperts were identified and invited to participate in the study. An Open Data Kit (ODK) was used to administer the questionnaire. Variables included, in addition to the 29 DHS asset items, sociodemographic information such as \u003cem\u003eage, sex, marital status, basic training received, level of education, year of professional experience, type of institution, researcher\u003c/em\u003e, and \u003cem\u003egrade\u003c/em\u003e while participant exposure to DHS included \u003cem\u003eaccess to DHS data, training on DHS, involvement in the design of the DHS\u003c/em\u003e, and \u003cem\u003einvolvement in the analyzes DHS data.\u003c/em\u003e\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eThe first step was to present the socio-demographic characteristics of the experts surveyed. This was done using absolute and relative frequencies. The reliability of the questionnaire was assessed using the Cronbach's alpha coefficient (with 95% confidence interval (CI)) to ensure consistency among DHS asset items. Using a 5-point Likert scale (strongly agree, agree, neither agree nor disagree, disagree or strongly disagree), experts graded DHS household assets\u0026rsquo; likelihood to measure SES. The 29 asset items were grouped into a binary variable where 1 corresponds to agree or strongly agree, and 0 otherwise. The prior probability for each item was calculated as the proportion of participants who agreed or strongly agreed for the corresponding item. Data were collected, entered into ODK and analysed with R software version 4.3.0.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the Socio-demographic characteristics of the experts\u0026rsquo; interview. We included a total of 67 experts amongst which, 50 (74.6%) males and 17 (25.4) females. Regarding the marital status, 47% of the experts were married and 47% lived alone. Education levels in the sample show a significant concentration in the fields of Statistics and Demography, with 35 participants (52.2%) and 21 participants (31.3%) respectively. Other fields, such as Economics, Medicine, and Nutrition, were less represented. Work experience varied among participants, with notable representation in all ranges. The largest proportion, 31.8% of participants, had between 8 and 30 years of experience, while 27.3% had between 6 and 8 years.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDistribution of sociodemographic information of experts\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrequency n (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17 (25.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50 (74.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge mean (sd)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33.7 (5.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u0026ndash;29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14 (23.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u0026ndash;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26 (42.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u0026ndash;39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14 (23.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40\u0026ndash;59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7 (11.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31 (47.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCohabiting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3 (4.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31 (47.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWidowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1 (1.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDemographer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21 (31.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEconomist\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3 (4.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMathematics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1 (1.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNutrition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1 (1.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReproductive health\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1 (1.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePublic health\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2 (3.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial Sciences\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1 (1.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStatistics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e35 (52.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedicine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2 (3.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of years of experience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16 (24.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11 (16.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u0026ndash;7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18 (27.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8 or more\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21 (31.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eReliability of the questionnaires\u003c/h2\u003e \u003cp\u003eThe internal consistency in the 29 item retained in the survey was excellent with Cronbach's alpha is 0.943 (95 CI: 0.920\u0026ndash;0.961)\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eParticipant exposure to DHS\u003c/h3\u003e\n\u003cp\u003eAll the experts expressed their familiarity with the Demographic and Health Surveys (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Similarly, 73% of experts stated that they used DHS data. However, they are less involved in the design and implementation of these surveys. Regarding the variables used in the DHS to measure socio-economic status, 38% of the respondents indicated that they were familiar with them.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eLevel of expert opinion on the 29 variables to measure economic status\u003c/h3\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the expert opinion on the 29 variables identified to measure household economic status. Responses on a scale of 1 to 5 (1\u0026thinsp;=\u0026thinsp;strongly disagree; 2\u0026thinsp;=\u0026thinsp;disagree; 3\u0026thinsp;=\u0026thinsp;neither agree nor disagree; 4\u0026thinsp;=\u0026thinsp;Agree; 5\u0026thinsp;=\u0026thinsp;strongly agree). Expert opinions varied from item to item and from grade to grade. For instance, television and cell phone ownership received high levels of agreement, with 52.3% and 43.1% of participants, respectively, indicating strong agreement. Conversely, certain items like the motorboat showed a more divided opinion with only 26.2% strongly agreeing about its contribution to SES. Ownership of household appliances, such as the refrigerator and stove, also showed a significant level of positive response, with 43.1% and 36.9% of respondents agreeing or strongly agreeing on their importance, respectively. In contrast, lower agreement levels for items like CD/DVD players (only 18.5% agreeing) indicate that such items may not carry as much economic significance. Regarding transportation items, car/truck ownership showed impressive support, with 38.5% agreeing and 33.8% strongly agreeing. In contrast, the animal-drawn cart received more mixed opinions. Computer tablet and laptop/notebook also received substantial positive responses, computer tablet attracted the highest agreement (47.7%).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDistribution of selection of economic variables and expert opinion\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStrongly disagree\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDisagree\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNeither Agree Nor disagree\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAgree\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eStrongly agree\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRadio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6 (9.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14 (21.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11 (16.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e25 (38.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9 (13.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTelevision\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3 (4.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2 (3.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6 (9.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e34 (52.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e20 (30.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHome phone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12 (18.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10 (15.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9 (13.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e18 (27.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e16 (24.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDesktop computer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6 (9.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8 (12.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9 (13.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e22 (33.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e20 (30.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRefrigerator\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3 (4.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3 (4.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4 (6.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e28 (43.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e27 (41.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStove\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3 (4.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6 (9.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11 (16.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e21 (32.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e24 (36.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGaz stove\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1 (1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6 (9.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11 (16.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e26 (40.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e21 (32.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAir conditioner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6 (9.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9 (13.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11 (16.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16 (24.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e23 (35.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1 (1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9 (13.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14 (21.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e26 (40.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15 (23.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD/DVD player\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9 (13.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21 (32.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16 (24.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12 (18.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7 (10.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrinding mill\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6 (9.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15 (23.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16 (24.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e24 (36.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4 (6.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMixer/Grinder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2 (3.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4 (6.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8 (12.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e33 (50.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e18 (27.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModem/Internet key\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2 (3.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6 (9.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8 (12.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e26 (40.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e23 (35.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCable/Satellite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2 (3.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4 (6.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8 (12.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e28 (43.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e23 (35.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGenerator\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8 (12.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3 (4.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13 (20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17 (26.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e24 (36.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSolar panel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6 (9.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3 (4.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14 (21.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16 (24.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e26 (40.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWater pump\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3 (4.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3 (4.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13 (20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17 (26.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e29 (44.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClock/pendulum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7 (10.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13 (20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16 (24.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e23 (35.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6 (9.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWatch\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6 (9.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16 (24.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13 (20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e23 (35.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7 (10.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCell phone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3 (4.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5 (7.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7 (10.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e22 (33.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e28 (43.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBicycle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6 (9.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14 (21.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21 (32.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15 (23.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9 (13.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMotocycle/scooter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3 (4.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10 (15.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18 (27.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19 (29.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15 (23.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnimal-drawn cart\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11 (16.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10 (15.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20 (30.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13 (20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11 (16.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCar/truck\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6 (9.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3 (4.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9 (13.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e25 (38.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e22 (33.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMotorboat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13 (20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12 (18.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10 (15.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13 (20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e17 (26.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLaptop/notebook\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1 (1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6 (9.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6 (9.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e28 (43.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e24 (36.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComputer tablet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3 (4.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6 (9.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8 (12.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e31 (47.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e17 (26.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgricultural land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2 (3.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4 (6.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7 (10.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e27 (41.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e25 (38.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFarm animals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4 (6.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10 (15.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12 (18.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e23 (35.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e16 (24.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSocio economic variables according to experts' preferences\u003c/h2\u003e \u003cp\u003eWe have grouped each item into a binary variable where 1 corresponds to agree or strongly agree responses, and 0 corresponds to the other modalities. Responses were quantified in terms of frequency in descending order and presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Furthermore, the probabilities of saying \"yes\" to the various items were also presented in the same table.\u003c/p\u003e \u003cp\u003eThe probabilities of validating an item as a measure economic of level varied from 0.016 to 0.047. Thus, the variables most likely to measure economic status include refrigerator (0.047), television (0.046), laptop (0.044), mixer (0.043), computer tablet (0.043), agricultural land (0.043), cable (0.042), cell phone (0.042), modem (0.041), water pump (0.041), and car (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDistribution of economic variables according to experts' preferences and probability of selection of each item\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProbability\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRefrigerator\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e58 (85.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTelevision\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e57 (83.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLaptop/notebook\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e54 (79.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMixer/Grinder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e53 (77.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComputer tablet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e53 (77.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgricultural land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e53 (77.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCable/Satellite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e52 (76.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCell phone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e52 (76.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModem/Internet key\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50 (73.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWater pump\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e49 (72.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCar/truck\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e49 (72.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGas stove\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e49 (72.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStove\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e48 (70.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDesktop computer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45 (66.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSolar panel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e44 (64.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e43 (64.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGenerator\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e43 (63.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAir conditioner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e41 (60.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFarm animals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e41 (60.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRadio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e36 (52.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHome phone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e36 (52.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMotocycle/scooter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34 (50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMotorboat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32 (47.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClock/pendulum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31 (45.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWatch\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31 (45.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrinding mill\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29 (42.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBicycle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24 (35.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnimal-drawn cart\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24 (35.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD/DVD player\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20 (29.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eSES is a combination of factors that affect the health condition of an individual or a family. These factors may include education, income, and employment type [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Measuring SES level through indicators of ownership of goods is a common approach in public health and social research. The DHS are widely used survey tool that include items related to ownership of goods to assess household wealth and SES. These items are included in the DHS because of they may directly influences health outcome [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe most frequent approach to constructing an item index from a set of variables is a statistical method known as Principal Components Analysis (PCA). PCA is a technique for data reduction; it utilizes the correlations among indicators to create a series of uncorrelated principal component [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The objective of this study was is to propose an alternative to statistical and mathematical models based on expert opinion for selecting DHS variables used to measure SES and to obtain prior probabilities for each selected DHS item.\u003c/p\u003e \u003cp\u003eThe selection of ownership goods indicators can provide valuable insights into the living standards of households. Our findings indicate that the experts identified ownership of refrigerator, television, laptop/notebook, and mixer/grinder and computer tablet as the most important indicators in measuring socioeconomic status. These results show a clear consensus regarding certain items, such as refrigerators and televisions, which were strongly agreed by the experts as critical indicators of SES. The overpowering agreement on these items highlights their potential perceived importance in creating a stable household environment, as they are often associated with basic needs and comfort. In contrast, items such as motorboats and CD/DVD players received lower levels of agreement, suggesting that the relevance of certain items may be context-dependent and less associated with current socio-economic paradigms.\u003c/p\u003e \u003cp\u003eWhen looking at the probabilities assigned to various items to measure SES, items such as refrigerators and televisions emerged as the most strongly supported items, indicating their integral role in determining SES. The quantitative assessment offers a compelling argument for prioritising specific items in future versions of the DHS. By focusing on high-probability measures, policymakers and researchers can ensure that the most relevant indicators are incorporated into assessments of socio-economic well-being, thereby improving the reliability and validity of the conclusions drawn.\u003c/p\u003e \u003cp\u003eIn addition, the observation that technological items such as laptops and tablet computers feature prominently among the selected variables reflects a wider socio-economic trend towards digital inclusion. Access to technology is increasingly seen as a determinant of economic status, influencing opportunities for education, employment and social mobility. The implications of this shift suggest that measures of socio-economic status must continually evolve, incorporating emerging item classes that respond to changing economic landscapes.\u003c/p\u003e \u003cp\u003eOther studies have suggested similar items to estimate socio-economic status and inequality within populations [\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. A related study employed the Delphi method to develop shortened wealth indices using survey data from 16 countries, for Cameroon, 9 items were identified of which, 6 items (Television, Cable/Satellite, Refrigerator, Fan Mixer/Grinder, Watch) were related to items [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Similarly, a study conducted in Iran concluded that 6 items out of 33 in a simple item index, the items comprised of kitchen, bathroom, vacuum cleaner, washing machine, freezer, and personal computer [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], unlike our study that used expert opinions, this research employed statistical algorithm in order to reach a shortened list. Furthermore, According to the Delphi evaluation by expert consensus on Iran, 15 items were identified of which 7 (house ownership, ,personal computer/laptop, smart cell phone, 3D TV, dishwasher, microwave, and car ownership.) were related to items [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Comparisons of results between countries are challenging due to the difference in indicators selection, constructing approaches and items sets.\u003c/p\u003e \u003cp\u003eTo our knowledge, no study has assessed an alternative to statistical and mathematical models based on expert opinion for selecting DHS variables for measuring SES in Cameroon. Our study involved expert in diverse background implicated directly in the DHS, thus ensuring that the validated composite items developed in this study are generalizable to national-level data and can be applied to population subgroups.\u003c/p\u003e \u003cp\u003eMany studies highlighted some limits in suing the PCA approach, One notable limitation is the \u0026lsquo;urban bias\u0026rsquo;, as it is based on items that better reflect social stratification in urban areas than in rural areas. For example urban households are much more likely to have access to electricity compared to the rural households, while items that rural households may have, such as access to land and livestock, are less often considered [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Furthermore, a study conducted by Houweling et al. indicated that while PCA can provide a convenient way of constructing composite indices, it may yield odd results when applied to short lists of items [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eExpert opinion is essential when selecting indicators for socio-economic measurement. When selecting indicators, experts take into account factors such as cultural relevance, local context, and survey feasibility. Nevertheless, relying on expert opinion alone could potentially introduce biases based on subjective judgment, personal experiences, or limited perspectives.\u003c/p\u003e \u003cp\u003eIn this context, experts can provide prior beliefs on the relationship between ownership indicators and socio-economic status. These prior beliefs can be formalized as Bayesian priors and combined with observed data on ownership indicators to update and refine the estimation of socio-economic levels. Despite the fact that the expert opinion approach is an innovative method of measuring SES, the underlying subjectivity of expert opinion can be biased, so it is important to find a balance between expert opinion and empirical evidence. In support of this, our results recommend the development of a Bayesian model that integrates expert opinion and empirical evidence, which will improve the robustness of SES measurement by providing a more detailed insight into the socio-economic context.\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eAlthough the study provides a valuable contribution to the field, it is important to recognize its limits. Like other opinion survey studies, our study relies on judgement and perception. Our study is still reliable as the potential selection bias was reduced by obtaining a representative list of experts from the Cameroon National Institute of Statistics; and the internal consistency of the questionnaire was excellent.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study presents a new approach to the selection of variables from the DHS to measure SES, highlighting the importance of expert opinion. We were able to identify key indicators in particular the ownership of items such as fridges, televisions, laptops and other household appliances deemed essential for accurately assessing the economic situation of populations, particularly in the context of Cameroon. This research underscores the importance of integrating local expert insights to refine the measurement of SES, promoting improved health outcomes in populations, particularly in Cameroon. Future research should explore the application of this expert-opinion-driven framework in various contexts to create more comprehensive, robust, and reliable SES indicators\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfidence Interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDHS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDemographic and Health Surveys\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eODK\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOpen Data Kit\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOECD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOrganization for Economic Cooperation and Development\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePCA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePrincipal Components Analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSES\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSocioeconomic Status\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWHO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWorld Health Organization\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study obtained an ethical approval number (00/57/CRERSHC/2023) from the Regional Ethics Committee of Yaoundé, and was performed in accordance with the Declaration of Helsinki. Prior to the administration of each questionnaire, the surveyor fully explained to each expert on the basis of an information sheet the objectives and procedures of the study, and the verbal and written consent of those willing to participate was collected. Data anonymity and confidentiality were respected throughout the research process.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent from all participants included in this study was obtained\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data generated from this study are available on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare\u0026nbsp;no conflict of interest in this study\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study did not receive any funding from agency in the public and commercial sector.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGNT, ACM, CJ, CNB, and AH conceptualized the study. YNE, AN, JSEK, and MNN wrote the method and collected the data. GPLD, BBT, and CNB analyzed and interpreted the data. CNB, YNE, and MNN prepared the draft. CJ, AH, GNT, MNN, and ACM wrote and reviewed the manuscript. All authors gave final approval for submission.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would\u0026nbsp;like\u0026nbsp;to acknowledge and appreciate all stakeholders for accepting to participate in this study.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBraveman P, Gruskin S. Defining equity in health. J Epidemiol Community Health. 2003;57(4):254\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBraveman. Health disparities and health equity: concepts and measurement. Annu Rev Public Health. 2006;27(1):167\u0026ndash;94.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePenman-Aguilar A, Talih M, Huang D, Moonesinghe R, Bouye K, Beckles G. Measurement of Health Disparities, Health Inequities, and Social Determinants of Health to Support the Advancement of Health Equity. J Public Health Manag Pract. 2016;22(Suppl 1):S33\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCunningham RM, Ranney ML, Goldstick JE, Kamat SV, Roche JS, Carter PM. Federal Funding For Research On The Leading Causes Of Death Among Children And Adolescents. Health Aff (Millwood). 2019;38(10):1653\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDowd B, McKenney M, Boneva D, Elkbuli A. Disparities in National Institute of Health trauma research funding: The search for sufficient funding opportunities. Med (Baltim). 2020;99(6):e19027.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Health Organization. Closing the gap: Policy into practice on social determinants of health: discussion paper. World Health Organization. 2011. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://apps.who.int/iris/handle/10665/44731\u003c/span\u003e\u003cspan address=\"https://apps.who.int/iris/handle/10665/44731\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHosseinpoor AR, Bergen N, Koller T, Prasad A, Schlotheuber A, Valentine N, et al. Equity-oriented monitoring in the context of universal health coverage. PLoS Med. 2014;11(9):e1001727.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChakraborty NM, Fry K, Behl R, Longfield K. Simplified Asset Indices to Measure Wealth and Equity in Health Programs: A Reliability and Validity Analysis Using Survey Data From 16 Countries. Glob Health Sci Pract. 2016;4(1):141\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBraveman PA, Cubbin C, Egerter S, Chideya S, Marchi KS, Metzler M, et al. Socioeconomic status in health research: one size does not fit all. JAMA. 2005;294(22):2879\u0026ndash;88.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHowe LD, Galobardes B, Matijasevich A, Gordon D, Johnston D, Onwujekwe O, et al. Measuring socio-economic position for epidemiological studies in low- and middle-income countries: a methods of measurement in epidemiology paper. Int J Epidemiol. 2012;41(3):871\u0026ndash;86.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHowe LD, Hargreaves JR, Huttly SR. Issues in the construction of wealth indices for the measurement of socio-economic position in low-income countries. Emerg Themes Epidemiol. 2008;5:3.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHowe LD, Hargreaves JR, Ploubidis GB, De Stavola BL, Huttly SR. Subjective measures of socio-economic position and the wealth index: a comparative analysis. Health Policy Plan. 2011;26(3):223\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEyler L, Hubbard A, Juillard C. Assessment of economic status in trauma registries: A new algorithm for generating population-specific clustering-based models of economic status for time-constrained low-resource settings. Int J Med Inf. 2016;94:49\u0026ndash;58.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEyler L, Hubbard A, Juillard C. Optimization and validation of the EconomicClusters model for facilitating global health disparities research: Examples from Cameroon and Ghana. PLoS ONE. 2019;14(5):e0217197.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVyas S, Kumaranayake L. Constructing socio-economic status indices: how to use principal components analysis. Health Policy Plan. 2006;21(6):459\u0026ndash;68.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWani RT. Socioeconomic status scales-modified Kuppuswamy and Udai Pareekh's scale updated for 2019. J Family Med Prim Care. 2019;8(6):1846\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHowe LD, Galobardes B, Matijasevich A, Gordon D, Johnston D, Onwujekwe O, et al. Measuring socio-economic position for epidemiological studies in low- and middle-income countries: a methods of measurement in epidemiology paper. Int J Epidemiol. 2012;41(3):871\u0026ndash;86.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHowe LD, Hargreaves JR, Ploubidis GB, De Stavola BL, Huttly SR. Subjective measures of socio-economic position and the wealth index: a comparative analysis. Health Policy Plan. 2011;26(3):223\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePoirier MJP, Gr\u0026eacute;pin KA, Grignon M. Approaches and Alternatives to the Wealth Index to Measure Socioeconomic Status Using Survey Data: A Critical Interpretive Synthesis. Soc Indic Res. 2020;148(1):1\u0026ndash;46.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXie K, Marathe A, Deng X, Ruiz-Castillo P, Imputiua S, Elobolobo E et al. Alternative approaches for creating a wealth index: The case of Mozambique. BMJ Global Health. 2013;8(8), e012639.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTajik P, Majdzadeh R. Constructing pragmatic socioeconomic status assessment tools to address health equality challenges. Int J Prev Med. 2014;5(1):46\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShafiei S, Yazdani S, Jadidfard MP, Zafarmand AH. Measurement components of socioeconomic status in health-related studies in Iran. BMC Res Notes. 2019;12(1):70.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHouweling TA, Kunst AE, Mackenbach JP. Measuring health inequality among children in developing countries: does the choice of the indicator of economic status matter? Int J Equity Health. 2003;2(1):8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNguefack-Tsague G, Klasen S, Zucchini W. On weighting the components of the human development index: a statistical justification. J Hum Dev Capabilities. 2011;12(2):183\u0026ndash;202.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Prior Probability, Expert Opinion, Demographics and Health Survey, Socioeconomic Status, Health equity","lastPublishedDoi":"10.21203/rs.3.rs-5603503/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5603503/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eDespite increasing awareness of socioeconomic status\u0026rsquo;s (SES) association with health outcomes, there is no widely accepted and rapidly implementable estimation of SES measures in resource-limited settings. An exception is the Demographic and Health Surveys (DHS)\u0026rsquo;s wealth quintile index constructed from household ownership assets. To facilitate health equity surveillance, method of individual SES estimation requiring fewer number of household assets is needed. The objective of this study was to identify the DHS assets most relevant for measuring SES in Cameroon.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eParticipants interviewed with a structured questionnaire included stakeholders involved in the design and implementation of DHS in Cameroon for many years. Using a 5-point Likert scale, experts graded DHS assets\u0026rsquo; likelihood to measure SES. The questionnaire was strongly reliable (Cronbach\u0026rsquo;s alpha: 0.943, 95% CI: 0.920\u0026ndash;0.961, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) for using the 29 items retained to measure SES.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe probabilities of agreeing that an asset can be a useful measure of SES varied from 0.016 to 0.047. The 12 DHS assets most likely to measure SES included having \u003cem\u003eRefrigerator\u003c/em\u003e(85.3%), \u003cem\u003eTelevision\u003c/em\u003e(83.8%), \u003cem\u003eLaptop\u003c/em\u003e(79.4%), \u003cem\u003eMixer\u003c/em\u003e(77.9%), \u003cem\u003eComputer\u003c/em\u003e(77.9%), \u003cem\u003eAgricultural land\u003c/em\u003e(77.9%), \u003cem\u003eCable/Satellite\u003c/em\u003e(76.5%), \u003cem\u003eCell phone\u003c/em\u003e(76.5%), \u003cem\u003eModem/Internet key\u003c/em\u003e(73.5%), \u003cem\u003eWater pump\u003c/em\u003e(72.1%), \u003cem\u003eCar/truck\u003c/em\u003e(72.1%) and \u003cem\u003eGas stove\u003c/em\u003e(72.1%) with a respective probability (prior) of 0.047, 0.046, 0.044, 0.043, 0.043, 0.043, 0.042, 0.042, 0.041, 0.040, 0.040 and 0.040.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThis research underscores the importance of integrating local expert insights to refine the measurement of SES, promoting improved health outcomes in populations, particularly in Cameroon. Future research should explore the application of this expert-opinion-driven framework in various contexts to create more comprehensive, robust, and reliable SES indicators.\u003c/p\u003e","manuscriptTitle":"Estimating socioeconomic status for health equity surveillance in Cameroon: an expert opinion survey","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-03 08:41:30","doi":"10.21203/rs.3.rs-5603503/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3bc855e6-fc23-492e-8b5f-90c60113177a","owner":[],"postedDate":"February 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-02-11T03:08:22+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-03 08:41:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5603503","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5603503","identity":"rs-5603503","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}