Propensity score analysis for health care disparities: a deweighting approach.

BACKGROUND: Propensity score weighting is a useful tool to make causal or unconfounded comparisons between groups. According to the definition by the Institute of Medicine (IOM), estimates of health care disparities should be adjusted for health-status factors but not for socioeconomic status (SES) variables. There have been attempts to use propensity score weighting to generate estimates that are concordant with IOM's definition. However, the existing propensity score methods do not preserve SES distributions in minority and majority groups unless SES variables are independent of health status variables. METHODS: The present study introduces a deweighting method that uses two types of propensity scores. One is a function of all covariates of health status and SES variables and is used to weight study subjects to adjust for them. The other is a function of only the SES variables and is used to deweight the subjects to preserve the original SES distributions. RESULTS: The procedure of deweighting is illustrated using a dataset from a right heart catheterization (RHC) study, where it was used to examine whether there was a disparity between black and white patients in receiving RHC. The empirical example provided promising evidence that the deweighting method successfully preserved the marginal SES distributions for both racial groups but balanced the conditional distributions of health status given SES. CONCLUSIONS: Deweighting is a promising tool for implementing the IOM-definition of health care disparities. The method is expected to be broadly applied to quantitative research on health care disparities.

Associated factors of periodontitis and predicted study among young man in China: a population-based cross-sectional study.

BACKGROUND: Periodontitis represents the foremost oral condition in young men, strongly correlated with socioeconomic elements and oral health behaviors. This research aimed to assess the prevalence of periodontitis and associated associations with socio-demographics and oral health practices for subsequent Hazard Ratio (HR) estimation. METHODS: A total of 46,476 young men were recruited to the study between August 2022 and October 2023. A questionnaire on socio-demographic factors and oral health-related behaviors related to periodontitis was completed. The standard procedure was used for oral examination. Logistic regression and hazard ratios were used to estimate the influencing factors, whereas the nomogram was used to predict the risk of periodontitis in young men. RESULTS: A total of 46,476 young men were surveyed and completed the questionnaire. The overall prevalence of periodontitis among young men was 1.74%. Out of these, 1.7% had mild periodontitis and 0.6% had moderate periodontitis. Age and dental calculus were important factors in the periodontal health of young men. This nomogram, which includes 7 easily obtainable clinical characteristics routinely collected during periodontitis risk assessment, provides clinicians with a user-friendly tool to assess the risk of periodontal disease in young men. CONCLUSIONS: Regular dental prophylaxis is crucial for young men to maintain their gingival health and prevent the onset of periodontitis. Dental calculus plays a prominent role in this matter, as it serves as a significant contributing factor.

Characteristics of the health districts in Italy and their implication in primary health care policies: an analysis of socio-demographic trends.

Introduction: The Health District (HD) is a critical component of Italy's National Health Service, responsible for ensuring Primary Health Care (PHC) services in response to community health needs. The Italian government established a national strategic reform program, the National Recovery and Resilience Plan (PNRR), starting in 2022, with a series of health interventions to reorganize the PHC setting, the main reform being the Ministerial Decree 77/2022 (DM77). Our study aimed to provide a description of socio-demographic data and to assess the correlation between HDs, in order to suggest health intervention priorities in PHC reforms. Materials and methods: We conducted our analysis using a cross-sectional record linkage of data from multiple sources to compare organizational and socio-demographic variables. A dataset was created with each of the 21 Italian Regions' HDs data of population, land area, mean age, ageing index, old-age dependency ratio, birth rate and death rate. The Inland Areas Project data was integrated for a socio-economic perspective. Results: Our study identified comparable groups of HDs, considering demographical, socio-economic and geographical aspects. The study provides a baseline understanding of the Italian situation prior to the implementation of DM77. It also highlights that inhabitants number cannot be the only variable to take into account for the definition of Italian HDs organisation and PHC reform, providing intercorrelated variables that take into account geographic location, demographic data, and socio-economic aspects. Conclusion: By acknowledging the interplay of demographic, socio-economic, and geographic factors, policymakers can tailor interventions to address diverse community needs, ensuring a more effective and equitable PHC system.

Identification and Economic Evaluation of Differentiated Thyroid Cancer Care Consumption Patterns Using Sequence Analysis.

Objectives: This study aims to assess the impact of care consumption patterns and individual characteristics on the cost of treating differentiated thyroid carcinoma (DTC), in France, with a specific emphasis on socioeconomic position. Methods: The methodology involved a net cost approach utilising cases from the EVATHYR cohort and controls from the French National Health Insurance database. Care consumption patterns were created using Optimal Matching and clustering techniques. The individual characteristics influence on patterns was assessed using multinomial logistic regression. The individual characteristics and patterns influence on care costs was assessed using generalised estimating equations. Results: The findings revealed an average cost of €13,753 per patient during the initial 3 years. Regression models suggested the main predictors of high DTC specific care consumption tended to include having a high risk of cancer recurrence (OR = 4.97), being a woman (OR = 2.00), and experiencing socio-economic deprivation (OR = 1.26), though not reaching statistical significance. Finally, high DTC-specific care consumers also incurred higher general care costs (RR = 1.35). Conclusion: The study underscores the increased costs of managing DTC, shaped by consumption habits and socioeconomic position, emphasising the need for more nuanced DTC management strategies.

California's COVID-19 Vaccine Equity Policy: Cases, Hospitalizations, And Deaths Averted In Affected Communities.

In March 2021, California implemented a vaccine equity policy that prioritized COVID-19 vaccine allocation to communities identified as least advantaged by an area-based socioeconomic measure, the Healthy Places Index. We conducted quasi-experimental and counterfactual analyses to estimate the effect of this policy on COVID-19 vaccination, case, hospitalization, and death rates. Among prioritized communities, vaccination rates increased 28.4 percent after policy implementation. Furthermore, an estimated 160,892 COVID-19 cases, 10,248 hospitalizations, and 679 deaths in the least-advantaged communities were averted by the policy. Despite these improvements, the share of COVID-19 cases, hospitalizations, and deaths in prioritized communities remained elevated. These estimates were robust in sensitivity analyses that tested exchangeability between prioritized communities and those not prioritized by the policy; model specifications; and potential temporal confounders, including prior infections. Correcting for disparities by strategically allocating limited resources to the least-advantaged or most-affected communities can reduce the impacts of COVID-19 and other diseases but might not eliminate health disparities.

Dental Care Among Adults Age 65 and Older: United States, 2022.

Oral health is associated with overall health, especially in older adults (age 65 and older). Chronic conditions in older adults may affect oral health, and poor oral health may increase the risk of certain chronic conditions (1-3). Poor oral health has also been associated with increased cardiovascular disease risk (4). Several factors, including chronic conditions, health status, race, and income have been associated with reduced dental care use among older adults (5-9). This report describes the percentage of older adults who had a dental visit in the past 12 months by selected sociodemographic characteristics and chronic conditions using the 2022 National Health Interview Survey (NHIS). .

Analysing district-level climate vulnerability pattern in Madhya Pradesh, India, based on agricultural and socio-economic indicators.

Indian agriculture transitioned from a food deficit sector to a food surplus following the Green Revolution. However, the continued progress of Indian agriculture has been hampered by climate change. This research explores the district-wise vulnerability in Madhya Pradesh, India, to climate change by assessing the composite vulnerability index using the agricultural vulnerability index (AVI) and socio-economic vulnerability index (SEVI). The study seeks to understand how agricultural and socio-economic factors lead to variations in vulnerability across districts and influence targeted adaptation and mitigation strategies. The trend analysis results present declining rainfall and inclining temperature from 1951 to 2021 in Madhya Pradesh, directly affecting the agricultural sector and human livelihood. The composite vulnerability index (CVI) results revealed that districts with low values (< 0.394), such as Burhanpur and Balaghat, demonstrate reduced susceptibility due to limited cultivation, low reliance on rainfall, lower drought susceptibility, and decreased population density. Districts such as Panna and Bhopal show moderate vulnerability (0.394-0.423), with lower fallow land, reduced rainfed agriculture, and socio-economic vulnerability. Extensive agriculture and marginalised workers' presence influence high vulnerability (0.423 to 0.456) in districts such as Tikamgarh and Indore. Districts like Barwani and Jhabua have the highest CVI values (> 0.456), indicating substantial susceptibility to climate impacts. The cluster analysis validates the results of the vulnerability index. The findings highlight the urgent need for tailored adaptation strategies to address the diverse agricultural and socio-economic indicators creating vulnerability in Madhya Pradesh. The study helps understand regional vulnerability patterns and provides evidence-based policy approaches for resilience to climate change effects.

Spatial pattern analysis of the impact of community food environments on foetal macrosomia, preterm births and low birth weight.

Community food environments (CFEs) have a strong impact on child health and nutrition and this impact is currently negative in many areas. In the Republic of Argentina, there is a lack of research evaluating CFEs regionally and comprehensively by tools based on geographic information systems (GIS). This study aimed to characterize the spatial patterns of CFEs, through variables associated with its three dimensions (political, individual and environmental), and their association with the spatial distribution in urban localities in Argentina. CFEs were assessed in 657 localities with ≥5,000 inhabitants. Data on births and CFEs were obtained from nationally available open-source data and through remote sensing. The spatial distribution and presence of clusters were assessed using hotspot analysis, purely spatial analysis (SaTScan), Moran's Index, semivariograms and spatially restrained multivariate clustering. Clusters of low risk for LBW, macrosomia, and preterm births were observed in the central-east part of the country, while high-risk clusters identified in the North, Centre and South. In the central-eastern region, low-risk clusters were found coinciding with hotspots of public policy coverage, high night-time light, social security coverage and complete secondary education of the household head in areas with low risk for negative outcomes of the birth variables studied, with the opposite with regard to households with unsatisfied basic needs and predominant land use classes in peri-urban areas of crops and herbaceous cover. These results show that the exploration of spatial patterns of CFEs is a necessary preliminary step before developing explanatory models and generating novel findings valuable for decision-making.

Multilevel analysis of dropout from maternal continuum of care and its associated factors: Evidence from 2022 Tanzania Demographic and Health Survey.

BACKGROUND: The maternal continuum of care (CoC) is a cost-effective approach to mitigate preventable maternal and neonatal deaths. Women in developing countries, including Tanzania, face an increased vulnerability to significant dropout rates from maternal CoC, and addressing dropout from the continuum remains a persistent public health challenge. METHOD: This study used the 2022 Tanzania Demographic and Health Survey (TDHS). A total weighted sample of 5,172 women who gave birth in the past 5 years and had first antenatal care (ANC) were included in this study. Multilevel binary logistic regression analyses were used to examine factors associated with dropout from the 3 components of maternal CoC (i.e., ANC, institutional delivery, and postnatal care (PNC)). RESULTS: The vast majority, 83.86% (95% confidence interval (CI): 82.83%, 84.83%), of women reported dropout from the maternal CoC. The odds of dropout from the CoC was 36% (AOR = 0.64, (95% CI: 0.41, 0.98)) lower among married women compared to their divorced counterparts. Women who belonged to the richer wealth index reported a 39% (AOR = 0.61, (95% CI: 0.39, 0.95)) reduction in the odds of dropout, while those belonged to the richest wealth index demonstrated a 49% (AOR = 0.51, (95% CI: 0.31, 0.82)) reduction. The odds of dropout from CoC was 37% (AOR = 0.63, (95% CI: 0.45,0.87)) lower among women who reported the use of internet in the past 12 months compared to those who had no prior exposure to the internet. Geographical location emerged as a significant factor, with women residing in the Northern region and Southern Highland Zone, respectively, experiencing a 44% (AOR = 0.56, 95% CI: 0.35-0.89) and 58% (AOR = 0.42, 95% CI: 0.26-0.68) lower odds of dropout compared to their counterparts in the central zone. CONCLUSION: The dropout rate from the maternity CoC in Tanzania was high. The findings contribute to our understanding of the complex dynamics surrounding maternity care continuity and underscore the need for targeted interventions, considering factors such as marital status, socioeconomic status, internet usage, and geographical location.

Assessment of socioeconomic and racial differences in patients undergoing concurrent gynecologic oncology and urogynecology surgeries: a National Inpatient Sample (NIS) database study.

OBJECTIVE: To assess social determinants of health impacting patients undergoing gynecologic oncology versus combined gynecologic oncology and urogynecology surgeries. METHODS: We identified patients who underwent gynecologic oncology surgeries from 2016 to 2019 in the National Inpatient Sample using the International Classification of Diseases-10 codes. Demographics, including race and insurance status, were compared for patients who underwent gynecologic oncology procedures only (Oncologic) and those who underwent concurrent incontinence or pelvic organ prolapse procedures (Urogynecologic-Oncologic). A logistic regression model assessed variables of interest after adjustment for other relevant variables. RESULTS: From 2016 to 2019 the National Inpatient Sample database contained 389 (1.14%) Urogynecologic-Oncologic cases and 33 796 (98.9%) Oncologic cases. Urogynecologic-Oncologic patients were less likely to be white (62.1% vs 68.8%, p=0.02) and were older (median 67 vs 62 years, p<0.001) than Oncologic patients. The Urogynecologic-Oncologic cohort was less likely to have private insurance as their primary insurance (31.9% vs 38.9%, p=0.01) and was more likely to have Medicare (52.2% vs 42.8%, p=0.01). After multivariable analysis, black (adjusted odds ratio (aOR) 1.41, 95% CI 1.05 to 1.89, p=0.02) and Hispanic patients (aOR 1.53, 95% CI 1.11 to 2.10, p=0.02) remained more likely to undergo Urogynecologic-Oncologic surgeries but the primary expected payer no longer differed significantly between the two groups (p=0.95). Age at admission, patient residence, and teaching location remained significantly different between the groups. CONCLUSIONS: In this analysis of a large inpatient database we identified notable racial and geographical differences between the cohorts of patients who underwent Urogynecologic-Oncologic and Oncologic procedures.

Determinants of self-reported chronic disease diagnoses among older persons in South Africa.

BACKGROUND:  Chronic diseases tend to affect the quality of life for older persons worldwide, especially in resource-constrained developing countries. Chronic diseases contribute to a large number of deaths among the population of South Africa. AIM:  This study examines the determinants of self-reported chronic disease diagnoses among older persons in South Africa. SETTING:  The study setting was South Africa. METHODS:  Cross-sectional data from the 2019 South Africa General Household Survey were analysed (n [weighted] = 4 887 334). We fitted a binary logistic regression model to determine the relationship between socio-demographic factors and being diagnosed with self-reported chronic diseases. RESULTS:  We found that at least 5 in 10 older persons were diagnosed with self-reported chronic disease. The bivariate findings showed that age, population group, sex, marital status, level of education, disability status, household composition and province were significantly associated with self-reported chronic disease diagnoses. At the multivariate level, we found that age, sex, population group, marital status, educational level, disability status, household wealth status, household composition and province were key predictors of self-reported chronic disease diagnoses. CONCLUSION:  We found that various factors were key determinants of being diagnosed with self-reported chronic diseases. This study offers important insights into the main correlations between older adults and self-reported chronic illness diagnoses. More study is required on the health of the elderly as it will help direct policy discussions and improve the development of health policies about the elderly.Contribution: This study highlights the need for a better understanding of, and continued research into, the determinants health among older populations to guide future healthcare strategies.

What is the burden of multimorbidity and the factors associated with its occurrence in elderly Brazilians?

OBJECTIVE: To estimate the prevalence of multimorbidity in elderly people and its association with sociodemographic characteristics, lifestyle, and anthropometry. METHODS: This was a cross-sectional study using data from the National Health Survey, 2019. A total of 22,728 elderly individuals from all 27 Brazilian states were randomly selected. Poisson regression models with robust variance were employed, and a significance level of 5% was adopted. RESULTS: The prevalence of multimorbidity was 51.6% (95% CI: 50.4-52.7), with the highest estimates observed in the South and Southeast. Multimorbidity was associated with being female (aPR = 1.33; 95% CI: 1.27-1.39), being 80 years old or older (aPR = 1.12; 95% CI: 1.05-1.19), having low education (aPR = 1.16; 95% CI: 1.07-1.25), past cigarette use (aPR = 1.16; 95% CI: 1.11-1.21), insufficient physical activity (aPR = 1.13; 95% CI: 1.06-1.21), and screen use for 3 hours or more per day (aPR = 1.13; 95% CI: 1.08-1.18). CONCLUSION: Multimorbidity affects more than half of the elderly population in Brazil and is associated with social, demographic, and behavioral factors.

Spatial distribution of arboviruses and its association with a social development index and the waste disposal in São Luís, state of Maranhão, Brazil, 2015 to 2019.

OBJECTIVE: To detect spatial and spatiotemporal clusters of urban arboviruses and to investigate whether the social development index (SDI) and irregular waste disposal are related to the coefficient of urban arboviruses detection in São Luís, state of Maranhão, Brazil. METHODS: The confirmed cases of Dengue, Zika and Chikungunya in São Luís, from 2015 to 2019, were georeferenced to the census tract of residence. The Bayesian Conditional Autoregressive regression model was used to identify the association between SDI and irregular waste disposal sites and the coefficient of urban arboviruses detection. RESULTS: The spatial pattern of arboviruses pointed to the predominance of a low-incidence cluster, except 2016. For the years 2015, 2016, 2017, and 2019, an increase of one unit of waste disposal site increased the coefficient of arboviruses detection in 1.25, 1.09, 1.23, and 1.13 cases of arboviruses per 100 thousand inhabitants, respectively. The SDI was not associated with the coefficient of arboviruses detection. CONCLUSION: In São Luís, spatiotemporal risk clusters for the occurrence of arboviruses and a positive association between the coefficient of arbovirus detection and sites of irregular waste disposal were identified.

Impacts of socioeconomic and environmental factors on neoplasms incidence rates using machine learning and GIS: a cross-sectional study in Iran.

Neoplasm is an umbrella term used to describe either benign or malignant conditions. The correlations between socioeconomic and environmental factors and the occurrence of new-onset of neoplasms have already been demonstrated in a body of research. Nevertheless, few studies have specifically dealt with the nature of relationship, significance of risk factors, and geographic variation of them, particularly in low- and middle-income communities. This study, thus, set out to (1) analyze spatiotemporal variations of the age-adjusted incidence rate (AAIR) of neoplasms in Iran throughout five time periods, (2) investigate relationships between a collection of environmental and socioeconomic indicators and the AAIR of neoplasms all over the country, and (3) evaluate geographical alterations in their relative importance. Our cross-sectional study design was based on county-level data from 2010 to 2020. AAIR of neoplasms data was acquired from the Institute for Health Metrics and Evaluation (IHME). HotSpot analyses and Anselin Local Moran's I indices were deployed to precisely identify AAIR of neoplasms high- and low-risk clusters. Multi-scale geographically weight regression (MGWR) analysis was worked out to evaluate the association between each explanatory variable and the AAIR of neoplasms. Utilizing random forests (RF), we also examined the relationships between environmental (e.g., UV index and PM2.5 concentration) and socioeconomic (e.g., Gini coefficient and literacy rate) factors and AAIR of neoplasms. AAIR of neoplasms displayed a significant increasing trend over the study period. According to the MGWR, the only factor that significantly varied spatially and was associated with the AAIR of neoplasms in Iran was the UV index. A good accuracy RF model was confirmed for both training and testing data with correlation coefficients R2 greater than 0.91 and 0.92, respectively. UV index and Gini coefficient ranked the highest variables in the prediction of AAIR of neoplasms, based on the relative influence of each variable. More research using machine learning approaches taking the advantages of considering all possible determinants is required to assess health strategies outcomes and properly formulate policy planning.

Uncovering physical activity trade-offs in transportation policy: A spatial agent-based model of Bogotá, Colombia.

BACKGROUND: Transportation policies can impact health outcomes while simultaneously promoting social equity and environmental sustainability. We developed an agent-based model (ABM) to simulate the impacts of fare subsidies and congestion taxes on commuter decision-making and travel patterns. We report effects on mode share, travel time and transport-related physical activity (PA), including the variability of effects by socioeconomic strata (SES), and the trade-offs that may need to be considered in the implementation of these policies in a context with high levels of necessity-based physical activity. METHODS: The ABM design was informed by local stakeholder engagement. The demographic and spatial characteristics of the in-silico city, and its residents, were informed by local surveys and empirical studies. We used ridership and travel time data from the 2019 Bogotá Household Travel Survey to calibrate and validate the model by SES. We then explored the impacts of fare subsidy and congestion tax policy scenarios. RESULTS: Our model reproduced commuting patterns observed in Bogotá, including substantial necessity-based walking for transportation. At the city-level, congestion taxes fractionally reduced car use, including among mid-to-high SES groups but not among low SES commuters. Neither travel times nor physical activity levels were impacted at the city level or by SES. Comparatively, fare subsidies promoted city-level public transportation (PT) ridership, particularly under a 'free-fare' scenario, largely through reductions in walking trips. 'Free fare' policies also led to a large reduction in very long walking times and an overall reduction in the commuting-based attainment of physical activity guidelines. Differential effects were observed by SES, with free fares promoting PT ridership primarily among low-and-middle SES groups. These shifts to PT reduced median walking times among all SES groups, particularly low-SES groups. Moreover, the proportion of low-to-mid SES commuters meeting weekly physical activity recommendations decreased under the 'freefare' policy, with no change observed among high-SES groups. CONCLUSIONS: Transport policies can differentially impact SES-level disparities in necessity-based walking and travel times. Understanding these impacts is critical in shaping transportation policies that balance the dual aims of reducing SES-level disparities in travel time (and time poverty) and the promotion of choice-based physical activity.

Multilevel analysis of determinants in postnatal care utilisation among mother-newborn pairs in India, 2019-21.

Background: Postnatal care (PNC) utilisation within 24 hours of delivery is a critical component of health care services for mothers and newborns. While substantial geographic variations in various health outcomes have been documented in India, there remains a lack of understanding regarding PNC utilisation and underlying factors accounting for these geographic variations. In this study, we aimed to partition and explain the variation in PNC utilisation across multiple geographic levels in India. Methods: Using India's 5th National Family Health Survey (2019-21), we conducted four-level logistic regression analyses to partition the total geographic variation in PNC utilisation by state, district, and cluster levels, and to quantify how much of theses variations are explained by a set of 12 demographic, socioeconomic, and pregnancy-related factors. We also conducted analyses stratified by selected states/union territories. Results: Among 149 622 mother-newborn pairs, 82.29% of mothers and 84.92% of newborns were reported to have received PNC within 24 hours of delivery. In the null model, more than half (56.64%) of the total geographic variation in mother's PNC utilisation was attributed to clusters, followed by 26.06% to states/union territories, and 17.30% to districts. Almost 30% of the between-state variation in mother's PNC utilisation was explained by the demographic, socioeconomic, and pregnancy-related factors (i.e. state level variance reduced from 0.486 (95% confidence interval (CI) = 0.238, 0.735) to 0.320 (95% CI = 0.152, 0.488)). We observed consistent results for newborn's PNC utilisation. State-specific analyses showed substantial geographic variation attributed to clusters across all selected states/union territories. Conclusions: Our findings highlight the consistently large cluster variation in PNC utilisation that remains unexplained by compositional effects. Future studies should explore contextual drivers of cluster variation in PNC utilisation to inform and design interventions aimed to improve maternal and child health.

Health Care Access and COVID-19 Vaccination in the United States: A Cross-Sectional Analysis.

BACKGROUND: Although federal legislation made COVID-19 vaccines free, inequities in access to medical care may affect vaccine uptake. OBJECTIVE: To assess whether health care access was associated with uptake and timeliness of COVID-19 vaccination in the United States. DESIGN: A cross-sectional study. SETTING: 2021 National Health Interview Survey (Q2-Q4). SUBJECTS: In all, 21,532 adults aged≥18 were included in the study. MEASURES: Exposures included 4 metrics of health care access: health insurance, having an established place for medical care, having a physician visit within the past year, and medical care affordability. Outcomes included receipt of 1 or more COVID-19 vaccines and receipt of a first vaccine within 6 months of vaccine availability. We examined the association between each health care access metric and outcome using logistic regression, unadjusted and adjusted for demographic, geographic, and socioeconomic covariates. RESULTS: In unadjusted analyses, each metric of health care access was associated with the uptake of COVID-19 vaccination and (among those vaccinated) early vaccination. In adjusted analyses, having health coverage (adjusted odds ratio [AOR] 1.60; 95% CI: 1.39, 1.84), a usual place of care (AOR 1.58; 95% CI: 1.42, 1.75), and a doctor visit within the past year (AOR 1.45, 95% CI: 1.31, 1.62) remained associated with higher rates of COVID-19 vaccination. Only having a usual place of care was associated with early vaccine uptake in adjusted analyses. LIMITATIONS: Receipt of COVID-19 vaccination was self-reported. CONCLUSIONS: Several metrics of health care access are associated with the uptake of COVID-19 vaccines. Policies that achieve universal coverage, and facilitate long-term relationships with trusted providers, may be an important component of pandemic responses.

Determinants of nutritional status of children aged 6-59 months in the case of Itang special woreda, Gambella, Ethiopia.

Nutritional status is one of the most important causes of improper physical and mental development in children. The study attempts to assess the factors affecting the severity status of children aged 6-59 months' malnutrition based on the weight-for-age anthropometric index (z-score) and examine between-kebeles-level differences in determinants of the nutritional status of children. A community-based, cross-sectional study design was conducted from October 12 to November 12, 2022. A sample of 397 children aged 6-59 months primary data by applying multi-stage clustered sampling technique was used by considering their heterogeneity. The data were entered by SPSS and analyzed by using R version 3.4.0 and STATA 14.2 statistical software package using a multilevel ordinal logistic regression model and inferences were conducted at a 5% significance level. The results show that birth interval ≥ 24 months (OR = 1.431253, 95% CI 1.221337 1.6763421, P-value = 0.008), economic status of households medium (OR = 16.21466, 95% CI 1.221403 1.423929, P-value = 0.000), economic status of households rich (OR = 223.2856, 95% CI 1.34295 2.582325, P-value = 0.000), employment status of the mother unemployed (OR = 0.2291348, 95% CI 0.0529511 0.9966281, P-value = 0.049), No toilet facility (bush field) (OR = 0.3163329, 95% CI 0.1825356 0.5481975, P-value = 0.000), number of household members (OR = 0.9100682, 95% CI 0.8313481 0.9967315, P-value = 0.042), breastfeeding < 12 months (OR = 0.53803, 95% CI 0.322315 0.898135, P-value = 0.018), educational level of father Primary (OR = 4.601687, 95% CI 1.758009 2.22053, P-value = 0.000), educational level of father Secondary above (OR = 99.65229, 95% CI 2.533502 4.788896, P-value = 0.000) and geographical area (kebeles) were found to be important factors that affect a child's nutritional status between 6 and 59 months. 15% of the overall variation is attributable to the Kebeles level, according to two-level multilevel ordinal logistic regressions with estimates of the variation attributable to the Kebeles level equal to 0.569 and an intraclass correlation coefficient of 0.15. Due to the nature of the response variable random intercept model with random coefficients fitted the data adequately in predicting the severity status of children aged 6-59 months' malnutrition for the multilevel ordinal logistic regression model analysis. So, the researcher recommended that implementing primary health care and nutrition programs that would fit each kebeles' features in Itang Special Woreda to safeguard children from nutritional deficiency.

Access to early diagnosis for attention-deficit/hyperactivity disorder among children and adolescents in Mexico City at specialized mental health services.

BACKGROUND: In Mexico, this pioneering research was undertaken to assess the accessibility of timely diagnosis of Dyads [Children and adolescents with Attention Deficit Hyperactivity Disorder (ADHD) and their primary caregivers] at specialized mental health services. The study was conducted in two phases. The first phase involved designing an "Access Pathway" aimed to identify barriers and facilitators for ADHD diagnosis; several barriers, with only the teacher being identified as a facilitator. In the second phase, the study aimed to determine the time taken for dyads, to obtain a timely diagnosis at each stage of the Access Pathway. As well as identify any disparities based on gender and socioeconomic factors that might affect the age at which children can access a timely diagnosis. METHOD: In a retrospective cohort study, 177 dyads participated. To collect data, the Acceda Survey was used, based on the robust Conceptual Model Levesque, 2013. The survey consisted of 48 questions that were both dichotomous and polytomous allowing the creation of an Access Pathway that included five stages: the age of perception, the age of search, the age of first contact with a mental health professional, the age of arrival at the host hospital, and the age of diagnosis. The data was meticulously analyzed using a comprehensive descriptive approach and a nonparametric multivariate approach by sex, followed by post-hoc Mann-Whitney's U tests. Demographic factors were evaluated using univariable and multivariable Cox regression analyses. RESULTS: 71% of dyads experienced a late, significantly late, or highly late diagnosis of ADHD. Girls were detected one year later than boys. Both boys and girls took a year to seek specialized mental health care and an additional year to receive a formal specialized diagnosis. Children with more siblings had longer delays in diagnosis, while caregivers with formal employment were found to help obtain timely diagnoses. CONCLUSIONS: Our findings suggest starting the Access Pathway where signs and symptoms of ADHD are detected, particularly at school, to prevent children from suffering consequences. Mental health school-based service models have been successfully tested in other latitudes, making them a viable option to shorten the time to obtain a timely diagnosis.

Big Data-Planetary Health approach for evaluating the Brazilian Dengue Control Program.

OBJECTIVE: This study aims to integrate the concepts of planetary health and big data into the Donabedian model to evaluate the Brazilian dengue control program in the state of São Paulo. METHODS: Data science methods were used to integrate and analyze dengue-related data, adding context to the structure and outcome components of the Donabedian model. This data, considering the period from 2010 to 2019, was collected from sources such as Department of Informatics of the Unified Health System (DATASUS), the Brazilian Institute of Geography and Statistics (IBGE), WorldClim, and MapBiomas. These data were integrated into a Data Warehouse. K-means algorithm was used to identify groups with similar contexts. Then, statistical analyses and spatial visualizations of the groups were performed, considering socioeconomic and demographic variables, soil, health structure, and dengue cases. OUTCOMES: Using climate variables, the K-means algorithm identified four groups of municipalities with similar characteristics. The comparison of their indicators revealed certain patterns in the municipalities with the worst performance in terms of dengue case outcomes. Although presenting better economic conditions, these municipalities held a lower average number of community healthcare agents and basic health units per inhabitant. Thus, economic conditions did not reflect better health structure among the three studied indicators. Another characteristic of these municipalities is urbanization. The worst performing municipalities presented a higher rate of urban population and human activity related to urbanization. CONCLUSIONS: This methodology identified important deficiencies in the implementation of the dengue control program in the state of São Paulo. The integration of several databases and the use of Data Science methods allowed the evaluation of the program on a large scale, considering the context in which activities are conducted. These data can be used by the public administration to plan actions and invest according to the deficiencies of each location.