BACKGROUND: There are limited population-representative data that describe the potential burden of Post-COVID conditions (PCC) in Mexico. We estimated the prevalence of PCC overall and by sociodemographic characteristics among a representative sample of adults previously diagnosed with COVID-19 in Mexico. We additionally, characterized the PCC symptoms, and estimated the association between diagnosed type-2 diabetes and hypertension with PCC.
METHODS: We used data from the 2021 National Health and Nutrition Survey in Mexico, a nationally and regionally representative survey, from August 1st to October 31st, 2021. Using the WHO definition, we estimated the prevalence of PCC by sociodemographics and prevalence of PCC symptoms. We fit multivariable log-binomial regression models to estimate the associations.
RESULTS: The prevalence of PCC was 37.0%. The most common persistent symptoms were fatigue (56.8%), myalgia or arthralgia (47.5%), respiratory distress and dyspnea (42.7%), headache (34.0%), and cough (25.7%). The prevalence was higher in older people, women, and individuals with low socioeconomic status. There was no significant association between hypertension and PCC or diabetes and PCC prevalence.
CONCLUSIONS: About one-third of the adult Mexican population who had COVID-19 in 2021 had Post-COVID conditions. Our population-based estimates can help assess potential priorities for PCC-related health services, which is critical in light of our weak health system and limited funding.

Introduction The pursuit of health care equity is a fundamental objective for Aotearoa New Zealand, and patient co-payments in primary care challenge this goal. Aim This study aimed to investigate the relationship between primary health care co-payments and the sociodemographic variables in areas where general practices provide health care. Methods Using census data, facilities information from the Ministry of Health, and socioeconomic deprivation indices, linear regression models were used to explore the relationship between weighted average fees charged by general practices and various sociodemographic variables in statistical area 2 regions. Results The study finds that areas with higher proportions of males and economically deprived individuals are associated with lower weighted average fees. Conversely, areas with higher proportions of retirement-aged and European individuals are linked with higher weighted average fees. The inclusion of the Very-Low-Cost-Access variable, indicating a subsidy scheme at the general practice level, made all the sociodemographic variables practically insignificant, suggesting Very-Low-Cost-Access practices are in the right geographical location to target high needs groups. Discussion The findings affirm the complexity of health care inequities in Aotearoa New Zealand, influenced not only by financial factors but also by demographic variables as they play out geographically. While subsidy schemes like the Very-Low-Cost-Access scheme appear to reach groups with greater need, a high level of unmet need due to cost suggests that the fees are still too high. Policymakers need to consider disparities in the on-going health care reforms and make further changes to subsidy schemes to reduce unmet need.

Introduction The inability to afford a consultation with a general practitioner may lead to delays in accessing care pathways. Aim This study aimed to explore the characteristics of people by their unmet need for a general practitioner consultation because of cost, and the characteristics of subsequent inpatient hospitalisations. Methods From the New Zealand Health Surveys (2013/14-2018/19), two groups were formed based on their unmet need for a general practitioner consultation due to cost. These groups were compared by socio-demographic factors and subsequent inpatient hospitalisation characteristics during follow-up. Time to an inpatient hospitalisation was the outcome in a proportional hazards regression model with need status as the key variable. The model was expanded to include confounding variables: sex, age group, ethnicity, the New Zealand Deprivation Index and self-rated health. Results The need group, characterised by having a higher proportion of females, younger adults, Māori, increased socioeconomic deprivation and poorer self-rated health experienced a greater chance of hospitalisation, a similar number of visits during follow-up, shorter stays and a quicker time to hospitalisation compared to the no-need group. Proportional hazards survival models gave a 28% higher hazard rate for the time to an inpatient hospitalisation for the need group compared to the no-need group. The inclusion of all the confounders in the model gave a similar hazard ratio. Discussion Although consultation fees vary across general practices, it is evident that this may not eliminate the cost barriers to accessing care for some groups. Needing multiple consultations may contribute to persistent unmet needs.

CONCLUSIONS: Depression is a common illness, it being the fourth-leading cause of disability in the world. The global lifetime prevalence falls between 8% and 12%, with female preponderance. A cross-sectional study was designed to assess the burden of depression among married women in a rural setting in North Kerala and identify its sociodemographic risk factors. The sample size was calculated to be 453 (prevalence = 24.9%; error = 20%; design-effect = 1.5). Systematic random sampling was used to select the participants from the eligible couple register. They were interviewed using MINI and Montgomery-Ashberg Depression Rating Scales. Descriptive analysis showed that 24.2% was suffering from current depression, mostly mild and none severe. Two percent reported past depression and 5.4% experienced spousal violence. Poor family support, experience of domestic violence, morbidity, and older spouses were found to be significant risk factors. The provision of mental health services by trained personnel and strict vigilance against the marriage of girls below the legal age and domestic violence are need of the hour.

Food insecurity is a public health concern that affects children worldwide, yet it represents a particular burden for low- and middle-income countries. This study aims to utilize machine learning to identify the associations between food insecurity and nutrient intake among children aged 5 to 18 years. The study\'s sample encompassed 1040 participants selected from a 2022 food insecurity household conducted in the West Bank, Palestine. The results indicated that food insecurity was significantly associated with dietary nutrient intake and sociodemographic factors, such as age, gender, income, and location. Indeed, 18.2% of the children were found to be food-insecure. A significant correlation was evidenced between inadequate consumption of various nutrients below the recommended dietary allowance and food insecurity. Specifically, insufficient protein, vitamin C, fiber, vitamin B12, vitamin B5, vitamin A, vitamin B1, manganese, and copper intake were found to have the highest rates of food insecurity. In addition, children residing in refugee camps experienced significantly higher rates of food insecurity. The findings emphasize the multilayered nature of food insecurity and its impact on children, emphasizing the need for personalized interventions addressing nutrient deficiencies and socioeconomic factors to improve children\'s health and well-being.

Self-rated health is an indicator that can be easily identified in health surveys, widely used to measure physical, social, mental, and health aspects of the population, and predict premature mortality. In Venezuela, this information only began to be collected recently, in the National Survey of Living Conditions (ENCOVI). In this context, our study aims to analyze the demographic and socioeconomic factors associated with non-positive self-rated health among Venezuelan adults. The ENCOVI 2021 (n = 16,803) was used as a data source, assessing a probability stratified sample with questions about health, education, emigration, and other social and economic aspects. Crude and adjusted prevalence ratio analyses were performed using Poisson regression models with robust variance. The prevalence of fair/bad self-rated health among Venezuelans was 17.8%. The results indicated a strong association between outcome prevalence and age group, 3.81 times higher (95%CI: 3.29-4.41) among individuals aged 60 or more when compared to individuals aged 18 to 29 years. Also, participants experiencing severe food insecurity had a prevalence 2 times higher (95%CI: 1.61-2.47) than those who did not have any level of food insecurity. Factors such as poverty, education, recent emigration of family members, and sex also showed a significant influence, also when analyzed independently. The results show that special attention should be dedicated to the health of individuals facing hunger and of the older people.

The COVID-19 pandemic provided an additional spotlight on the longstanding socioeconomic/health impacts of redlining and has added to the myriad of environmental justice issues, which has caused significant loss of life, health, and productive work. The Centers for Disease Control and Prevention (CDC) reports that a person with any selected underlying health conditions is more likely to experience severe COVID-19 symptoms, with more than 81% of COVID-19-related deaths among people aged 65 years and older. The effects of COVID-19 are not homogeneous across populations, varying by socioeconomic status, PM2.5 exposure, and geographic location. This variability is supported by analysis of existing data as a function of the number of cases and deaths per capita/1,00,000 persons. We investigate the degree of correlation between these parameters, excluding health conditions and age. We found that socioeconomic variables alone contribute to ~40% of COVID-19 variability, while socioeconomic parameters, combined with political affiliation, geographic location, and PM2.5 exposure levels, can explain ~60% of COVID-19 variability per capita when using an OLS regression model; socioeconomic factors contribute ~28% to COVID-19-related deaths. Using spatial coordinates in a Random Forest (RF) regressor model significantly improves prediction accuracy by ~120%. Data visualization products reinforce the fact that the number of COVID-19 deaths represents 1% of COVID-19 cases in the US and globally. A larger number of democratic voters, larger per-capita income, and age >65 years is negatively correlated (associated with a decrease) with the number of COVID cases per capita. Several distinct regions of negative and positive correlations are apparent, which are dominated by two major regions of anticorrelation: (1) the West Coast, which exhibits high PM2.5 concentrations and fewer COVID-19 cases; and (2) the middle portion of the US, showing mostly high number of COVID-19 cases and low PM2.5 concentrations. This paper underscores the importance of exercising caution and prudence when making definitive causal statements about the contribution of air quality constituents (such as PM2.5) and socioeconomic factors to COVID-19 mortality rates. It also highlights the importance of implementing better health/lifestyle practices and examines the impact of COVID-19 on vulnerable populations, particularly regarding preexisting health conditions and age. Although PM2.5 contributes comparable deaths (~7M) per year, globally as smoking cigarettes (~8.5M), quantifying any causal contribution toward COVID-19 is non-trivial, given the primary causes of COVID-19 death and confounding factors. This becomes more complicated as air pollution was reduced significantly during the lockdowns, especially during 2020. This statistical analysis provides a modular framework, that can be further expanded with the context of multilevel analysis (MLA). This study highlights the need to address socioeconomic and environmental disparities to better prepare for future pandemics. By understanding how factors such as socioeconomic status, political affiliation, geographic location, and PM2.5 exposure contribute to the variability in COVID-19 outcomes, policymakers and public health officials can develop targeted strategies to protect vulnerable populations. Implementing improved health and lifestyle practices and mitigating environmental hazards will be essential in reducing the impact of future public health crises on marginalized communities. These insights can guide the development of more resilient and equitable health systems capable of responding effectively to similar future scenarios.

BACKGROUND: The COVID-19 vaccination coverage shows variability in booster doses between residency areas or ethnicity. The aim of this study was to evaluate how sociodemographic conditions influence unequal vaccination coverage with booster doses against COVID-19 in Peru.
METHODS: A retrospective, ecological study with an evaluation of 196 provinces in Peru. The sociodemographic conditions were evaluated as sources of inequality (sex, age group, educational level, residence area, and ethnic group). The inequality measure used was the GINI, an index that show the inequal vaccination coverage with third and fourth booster doses against COVID-19 in Peruvians provinces. The index allow determinate a higher inequality when the value is near to 1, and a lower inequality when the value is near to 0. Also, the impact of each sociodemographic condition in the general inequality was evaluate with a decomposition analysis of GINI coefficient into Sk (composition effect), Gk (redistribution effect), Rk (differential effect).
RESULTS: In provinces evaluated the mean vaccine coverage for the third and fourth booster doses was 57.00% and 22.19%, respectively at twelve months since the beginning of vaccination campaign. The GINI coefficient was 0.33 and 0.31, for the third and fourth booster doses coverage, respectively. In the decomposition analysis, twelve months after the start of the third and fourth dose vaccination campaign, revealed higher Sk values for people living in rural areas (Sk = 0.94 vs. Sk = 2.39, respectively for third and fourth dose), while higher Gk values for Aymara (Gk = 0.92 vs. Gk = 0.92, respectively), Quechua (Gk = 0.53 vs. Gk = 0.53, respectively), and Afro-Peruvians (Gk = 0.61 vs. Gk = 0.61, respectively). Also, higher negative correlation in Rk values for people with elementary education (Rk=-0.43 vs. Rk=-0.33, respectively), aged between 15 and 19 years (Rk=-0.49 vs. Rk=-0.37, respectively), and Aymara (Rk=-0.51 vs. Rk=-0.66, respectively).
CONCLUSIONS: The rural residency area, lower education and Quechua, Aymara or Afro-Peruvians ethnicity determinated inequalities in vaccination coverage with booster doses against COVID-19 in Peruvian provinces.

Objectives-This report examines changes in telemedicine use among U.S. adults between 2021 and 2022 by selected sociodemographic and geographic characteristics. Methods-Data from the 2021 and 2022 National Health Interview Survey were used to assess changes between these 2 years in the percentage of adults who used telemedicine in the previous 12 months, by sex, age, race and Hispanic origin, family income, education, region of residence, urbanization level, and health insurance coverage. Results-Overall, the percentage of adults who used telemedicine in the past 12 months decreased from 37.0% in 2021 to 30.1% in 2022. This pattern was observed across several sociodemographic and geographic characteristics, such as sex, family income, education, region, and urbanization level. Women, adults with a college degree or higher, and adults living in more urban areas were all more likely to use telemedicine in 2022. In 2021 and 2022, uninsured adults ages 18-64 were less likely to use telemedicine compared with those who had private or public insurance, while adults age 65 and older who had Medicare only were less likely to use telemedicine compared with those with other types of insurance. However, for both age groups, telemedicine use decreased from 2021 to 2022 for all insurance types except public coverage for adults ages 18-64. Summary-National Health Interview Survey data may be used to monitor national trends and understand patterns of telemedicine use by sociodemographic and geographic characteristics as the transition forward from the global COVID-19 pandemic continues.

BACKGROUND: Asthma is the most common chronic respiratory illness among children in Australia. While childhood asthma prevalence varies by region, little is known about variations at the small geographic area level. Identifying small geographic area variations in asthma is critical for highlighting hotspots for targeted interventions. This study aimed to investigate small area-level variation, spatial clustering, and sociodemographic risk factors associated with childhood asthma prevalence in Australia.
METHODS: Data on self-reported (by parent/carer) asthma prevalence in children aged 0-14 years at statistical area level 2 (SA2, small geographic area) and selected sociodemographic features were extracted from the national Australian Household and Population Census 2021. A spatial cluster analysis was used to detect hotspots (i.e., areas and their neighbours with higher asthma prevalence than the entire study area average) of asthma prevalence. We also used a spatial Bayesian Poisson model to examine the relationship between sociodemographic features and asthma prevalence. All analyses were performed at the SA2 level.
RESULTS: Data were analysed from 4,621,716 children aged 0-14 years from 2,321 SA2s across the whole country. Overall, children\'s asthma prevalence was 6.27%, ranging from 0 to 16.5%, with significant hotspots of asthma prevalence in areas of greater socioeconomic disadvantage. Socioeconomically disadvantaged areas had significantly higher asthma prevalence than advantaged areas (prevalence ratio [PR] = 1.10, 95% credible interval [CrI] 1.06-1.14). Higher asthma prevalence was observed in areas with a higher proportion of Indigenous individuals (PR = 1.13, 95% CrI 1.10-1.17).
CONCLUSIONS: We identified significant geographic variation in asthma prevalence and sociodemographic predictors associated with the variation, which may help in designing targeted asthma management strategies and considerations for service enhancement for children in socially deprived areas.