Are Commonly Used Geographically Based Social Determinant of Health Indices in Orthopaedic Surgery Research Correlated With Each Other and With PROMIS Global-10 Physical and Mental Health Scores?

Geographically based social determinants of health (SDoH) measures are useful in research and policy aimed at addressing health disparities. In the United States, the Area Deprivation Index (ADI), Neighborhood Stress Score (NSS), and Social Vulnerability Index (SVI) are frequently used, but often without a clear reason as to why one is chosen over another. There is limited evidence about how strongly correlated these geographically based SDoH measures are with one another. Further, there is a paucity of research examining their relationship with patient-reported outcome measures (PROMs) in orthopaedic patients. Such insights are important in order to determine whether comparisons of policies and care programs using different geographically based SDoH indices to address health disparities in orthopaedic surgery are appropriate. Among new patients seeking care at an orthopaedic surgery clinic, (1) what is the correlation of the NSS, ADI, and SVI with one another? (2) What is the correlation of Patient-Reported Outcomes Measurement Information System (PROMIS) Global-10 physical and mental health scores and the NSS, ADI, and SVI? (3) Which geographically based SDoH index or indices are associated with presenting PROMIS Global-10 physical and mental health scores when accounting for common patient-level sociodemographic factors? New adult orthopaedic patient encounters at clinic sites affiliated with a tertiary referral academic medical center between 2016 and 2021 were identified, and the ADI, NSS, and SVI were determined. Patients also completed the PROMIS Global-10 questionnaire as part of routine care. Overall, a total of 75,335 new patient visits were noted. Of these, 62% (46,966 of 75,335) of new patient visits were excluded because of missing PROMIS Global-10 physical and mental health scores. An additional 2.2% of patients (1685 of 75,335) were excluded because they were missing at least one SDoH index at the time of their visit (for example, if a patient only had a Post Office box listed, the SDoH index could not be determined). This left 35% of the eligible new patient visits (26,684 of 75,335) in our final sample. Though only 35% of possible new patient visits were included, the diversity of these individuals across numerous characteristics and the wide range of sociodemographic status-as measured by the SDoH indices-among included patients supports the generalizability of our sample. The mean age of patients in our sample was 55 ± 18 years and a slight majority were women (54% [14,366 of 26,684]). Among the sample, 16% (4381of 26,684) of patients were of non-White race. The mean PROMIS Global-10 physical and mental health scores were 43.4 ± 9.4 and 49.7 ± 10.1, respectively. Spearman correlation coefficients were calculated among the three SDoH indices and between each SDoH index and PROMIS Global-10 physical and mental health scores. In addition, regression analysis was used to assess the association of each SDoH index with presenting functional and mental health, accounting for key patient characteristics. The strength of the association between each SDoH index and PROMIS Global-10 physical and mental health scores was determined using partial r-squared values. Significance was set at p < 0.05. There was a poor correlation between the ADI and the NSS (ρ = 0.34; p < 0.001). There were good correlations between the ADI and SVI (ρ = 0.43; p < 0.001) and between the NSS and SVI (ρ = 0.59; p < 0.001). There was a poor correlation between the PROMIS Global-10 physical health and NSS (ρ = -0.14; p < 0.001), ADI (ρ = -0.24; p < 0.001), and SVI (ρ = -0.17; p < 0.001). There was a poor correlation between PROMIS Global-10 mental health and NSS (ρ = -0.13; p < 0.001), ADI (ρ = -0.22; p < 0.001), and SVI (ρ = -0.17; p < 0.001). When accounting for key sociodemographic factors, the ADI demonstrated the largest association with presenting physical health (regression coefficient: -0.13 [95% CI -0.14 to -0.12]; p < 0.001) and mental health (regression coefficient: -0.13 [95% CI -0.14 to -0.12]; p < 0.001), as confirmed by the partial r-squared values for each SDoH index (physical health: ADI 0.04 versus SVI 0.02 versus NSS 0.01; mental health: ADI 0.04 versus SVI 0.02 versus NSS 0.01). This finding means that as social deprivation increases, physical and mental health scores decrease, representing poorer health. For further context, an increase in ADI score by approximately 36 and 39 suggests a clinically meaningful (determined using distribution-based minimum clinically important difference estimates of one-half SD of each PROMIS score) worsening of physical and mental health, respectively. Orthopaedic surgeons, policy makers, and other stakeholders looking to address SDoH factors to help alleviate disparities in musculoskeletal care should try to avoid interchanging the ADI, SVI, and NSS. Because the ADI has the largest association between any of the geographically based SDoH indices and presenting physical and mental health, it may allow for easier clinical and policy application. We suggest using the ADI as the geographically based SDoH index in orthopaedic surgery in the United States. Further, we caution against comparing findings in one study that use one geographically based SDoH index to another study's findings that incorporates another geographically based SDoH index. Although the general findings may be the same, the strength of association and clinical relevance could differ and have policy ramifications that are not otherwise appreciated; however, the degree to which this may be true is an area for future inquiry.

Bernstein DN ，Shin D ，Poolman RW ，Schwab JH ，Tobert DG ... -  《-》

Are Detailed, Patient-level Social Determinant of Health Factors Associated With Physical Function and Mental Health at Presentation Among New Patients With Orthopaedic Conditions?

It is well documented that routinely collected patient sociodemographic characteristics (such as race and insurance type) and geography-based social determinants of health (SDoH) measures (for example, the Area Deprivation Index) are associated with health disparities, including symptom severity at presentation. However, the association of patient-level SDoH factors (such as housing status) on musculoskeletal health disparities is not as well documented. Such insight might help with the development of more-targeted interventions to help address health disparities in orthopaedic surgery. (1) What percentage of patients presenting for new patient visits in an orthopaedic surgery clinic who were unemployed but seeking work reported transportation issues that could limit their ability to attend a medical appointment or acquire medications, reported trouble paying for medications, and/or had no current housing? (2) Accounting for traditional sociodemographic factors and patient-level SDoH measures, what factors are associated with poorer patient-reported outcome physical health scores at presentation? (3) Accounting for traditional sociodemographic factor patient-level SDoH measures, what factors are associated with poorer patient-reported outcome mental health scores at presentation? New patient encounters at one Level 1 trauma center clinic visit from March 2018 to December 2020 were identified. Included patients had to meet two criteria: they had completed the Patient-Reported Outcome Measure Information System (PROMIS) Global-10 at their new orthopaedic surgery clinic encounter as part of routine clinical care, and they had visited their primary care physician and completed a series of specific SDoH questions. The SDoH questionnaire was developed in our institution to improve data that drive interventions to address health disparities as part of our accountable care organization work. Over the study period, the SDoH questionnaire was only distributed at primary care provider visits. The SDoH questions focused on transportation, housing, employment, and ability to pay for medications. Because we do not have a way to determine how many patients had both primary care provider office visits and new orthopaedic surgery clinic visits over the study period, we were unable to determine how many patients could have been included; however, 9057 patients were evaluated in this cross-sectional study. The mean age was 61 ± 15 years, and most patients self-reported being of White race (83% [7561 of 9057]). Approximately half the patient sample had commercial insurance (46% [4167 of 9057]). To get a better sense of how this study cohort compared with the overall patient population seen at the participating center during the time in question, we reviewed all new patient clinic encounters (n = 135,223). The demographic information between the full patient sample and our study subgroup appeared similar. Using our study cohort, two multivariable linear regression models were created to determine which traditional metrics (for example, self-reported race or insurance type) and patient-specific SDoH factors (for example, lack of reliable transportation) were associated with worse physical and mental health symptoms (that is, lower PROMIS scores) at new patient encounters. The variance inflation factor was used to assess for multicollinearity. For all analyses, p values < 0.05 designated statistical significance. The concept of minimum clinically important difference (MCID) was used to assess clinical importance. Regression coefficients represent the projected change in PROMIS physical or mental health symptom scores (that is, the dependent variable in our regression analyses) accounting for the other included variables. Thus, a regression coefficient for a given variable at or above a known MCID value suggests a clinical difference between those patients with and without the presence of that given characteristic. In this manuscript, regression coefficients at or above 4.2 (or at and below -4.2) for PROMIS Global Physical Health and at or above 5.1 (or at and below -5.1) for PROMIS Global Mental Health were considered clinically relevant. Among the included patients, 8% (685 of 9057) were unemployed but seeking work, 4% (399 of 9057) reported transportation issues that could limit their ability to attend a medical appointment or acquire medications, 4% (328 of 9057) reported trouble paying for medications, and 2% (181 of 9057) had no current housing. Lack of reliable transportation to attend doctor visits or pick up medications (β = -4.52 [95% CI -5.45 to -3.59]; p < 0.001), trouble paying for medications (β = -4.55 [95% CI -5.55 to -3.54]; p < 0.001), Medicaid insurance (β = -5.81 [95% CI -6.41 to -5.20]; p < 0.001), and workers compensation insurance (β = -5.99 [95% CI -7.65 to -4.34]; p < 0.001) were associated with clinically worse function at presentation. Trouble paying for medications (β = -6.01 [95% CI -7.10 to -4.92]; p < 0.001), Medicaid insurance (β = -5.35 [95% CI -6.00 to -4.69]; p < 0.001), and workers compensation (β = -6.07 [95% CI -7.86 to -4.28]; p < 0.001) were associated with clinically worse mental health at presentation. Although transportation issues and financial hardship were found to be associated with worse presenting physical function and mental health, Medicaid and workers compensation insurance remained associated with worse presenting physical function and mental health as well even after controlling for these more detailed, patient-level SDoH factors. Because of that, interventions to decrease health disparities should focus on not only sociodemographic variables (for example, insurance type) but also tangible patient-specific SDoH characteristics. For example, this may include giving patients taxi vouchers or ride-sharing credits to attend clinic visits for patients demonstrating such a need, initiating financial assistance programs for necessary medications, and/or identifying and connecting certain patient groups with social support services early on in the care cycle. Level III, prognostic study.

Bernstein DN ，Lans A ，Karhade AV ，Heng M ，Poolman RW ，Schwab JH ，Tobert DG ... -  《-》

Are Social Deprivation and Low Traditional Health Literacy Associated With Higher PROMIS CAT Completion in Orthopaedic Surgery?

The Patient-Reported Outcomes Measurement Information System® (PROMIS®) may be used to assess an individual patient's perspective of their physical, mental, and social health through either standard or computer adaptive testing (CAT) patient questionnaires. These questionnaires are used across disciplines; however, they have seen considerable application in orthopaedic surgery. Patient characteristics associated with PROMIS CAT completion have not been examined within the context of social determinants of health, such as social deprivation or health literacy, nor has patient understanding of the content of PROMIS CAT been assessed. (1) What patient demographics, including social deprivation, are associated with completion of PROMIS CAT questionnaires? (2) Is health literacy level associated with completion of PROMIS CAT questionnaires? (3) Do patients with lower health literacy have a higher odds of completing PROMIS CAT without fully understanding the content? Between June 2022 and August 2022, a cross-sectional study was performed via a paper survey administered to patients at a single, urban, quaternary academic medical center in orthopaedic subspecialty clinics of foot and ankle, trauma, and hand/upper extremity surgeons. We considered all English-speaking patients aged 18 or older, including those with limited reading and/or writing abilities, as eligible provided they received an iPad in clinic to complete the PROMIS CAT questionnaire as part of their routine standard clinical care or they completed the questionnaire via a patient portal before the visit. In all, 946 patients were considered eligible during the study period and a convenience sample of 36% (339 of 946) of patients was approached for inclusion due to clinic time constraints. Fifteen percent (52 of 339) declined to participate, leaving 85% (287 of 339) of patients for analysis here. Median (range) age of study participants was 49 years (35 to 64). Fifty-eight percent (167 of 287) of study participants self-identified as non-Hispanic Black or African American and 26% (75 of 287) as non-Hispanic White. Even proportions were observed across education levels (high school graduate or less, 29% [82 of 287]; some college, 25% [73 of 287]; college graduate, 25% [71 of 287]; advanced degree, 20% [58 of 287]). Eighteen percent (52 of 287) of patients reported an annual income bracket of USD 0 to 13,000, and 17% (48 of 287) reported more than USD 120,000. Forty-six percent (132 of 287) of patients worked full-time, 21% (59 of 287) were retired, and 23% (66 of 287) were unemployed or on disability. The primary outcome of interest was self-reported PROMIS CAT questionnaire completion grouped as: fully completed, partially completed, or no part completed. Overall, self-reported PROMIS CAT questionnaire completion proportions were: 80% (229 of 287) full completion, 13% (37 of 287) partial completion, and 7% (21 of 287) no part completed. We collected the National Area Deprivation Index (ADI) score and the Brief Health Literacy Screening Tool (BRIEF) as part of the study survey to associate with level of completion. Additionally, patient understanding of PROMIS CAT was assessed through Likert-scaled responses to a study survey question that directly asked whether the patient understood all of the questions on the PROMIS CAT questionnaire. Responses to this question may have been limited by social desirability bias, and hence may overestimate how many individuals genuinely understood the questionnaire content. However, the benefit of this approach was it efficiently allowed us to estimate the ceiling effect of patient comprehension of PROMIS CAT and likely had a high degree of specificity for detecting lack of comprehension. ADI score adjusted for age was not associated with PROMIS CAT completion (partial completion OR 1.00 [95% CI 0.98 to 1.01]; p = 0.72, no part completed OR 1.01 [95% CI 0.99 to 1.03]; p = 0.45). Patients with lower health literacy scores, however, were more likely to not complete any part of their assigned questionnaires than patients with higher scores (no part completed OR 0.85 [95% CI 0.75 to 0.97]; p = 0.02). Additionally, 74% (26 of 35) of patients who did not fully understand all of the PROMIS CAT questionnaire questions still fully completed them-hence, 11% (26 of 229) of all patients who fully completed PROMIS CAT did not fully understand the content. Among patients self-reporting full completion of PROMIS CAT with health literacy data (99% [227 of 229]), patients with inadequate/marginal health literacy were more likely than patients with adequate health literacy to not fully understand all of the questions (21% [14 of 67] versus 8% [12 of 160], OR 3.26 [95% CI 1.42 to 7.49]; p = 0.005). Within an urban, socioeconomically diverse, orthopaedic patient population, health literacy was associated with PROMIS CAT questionnaire completion. Lower health literacy levels increased the likelihood of not completing any part of the assigned PROMIS CAT questionnaires. Additionally, patients completed PROMIS CAT without fully understanding the questions. This indicates that patient completion does not guarantee comprehension of the questions nor validity of their scores, even more so among patients with low health literacy. This is a substantive concern for fidelity of data gathered from PROMIS CAT. Clinical implementation of the PROMIS CAT in orthopaedic populations will benefit from further research into health literacy to increase questionnaire completion and to ensure that patients understand the content of the questions they are answering, which will increase the internal validity of the outcome measure.

Litvak AL ，Lin NA ，Hynes KK ，Strelzow JA ，Conti Mica MA ，Stepan JG ... -  《-》

A New Measure of Quantified Social Health Is Associated With Levels of Discomfort, Capability, and Mental and General Health Among Patients Seeking Musculoskeletal Specialty Care.

A better understanding of the correlation between social health and mindsets, comfort, and capability could aid the design of individualized care models. However, currently available social health checklists are relatively lengthy, burdensome, and designed for descriptive screening purposes rather than quantitative assessment for clinical research, patient monitoring, or quality improvement. Alternatives such as area deprivation index are prone to overgeneralization, lack depth in regard to personal circumstances, and evolve rapidly with gentrification. To fill this void, we aimed to identify the underlying themes of social health and develop a new, personalized and quantitative social health measure. (1) What underlying themes of social health (factors) among a subset of items derived from available legacy checklists and questionnaires can be identified and quantified using a brief social health measure? (2) How much of the variation in levels of discomfort, capability, general health, feelings of distress, and unhelpful thoughts regarding symptoms is accounted for by quantified social health? In this two-stage, cross-sectional study among people seeking musculoskeletal specialty care in an urban area in the United States, all English and Spanish literate adults (ages 18 to 89 years) were invited to participate in two separate cohorts to help develop a provisional new measure of quantified social health. In a first stage (December 2021 to August 2022), 291 patients rated a subset of items derived from commonly used social health checklists and questionnaires (Tool for Health and Resilience in Vulnerable Environments [THRIVE]; Protocol for Responding to and Assessing Patient Assets, Risks and Experiences [PRAPARE]; and Accountable Health Communities Health-Related Social Needs Screening Tool [HRSN]), of whom 95% (275 of 291; 57% women; mean ± SD age 49 ± 16 years; 51% White, 33% Hispanic; 21% Spanish speaking; 38% completed high school or less) completed all items required to perform factor analysis and were included. Given that so few patients decline participation (estimated at < 5%), we did not track them. We then randomly parsed participants into (1) a learning cohort (69% [189 of 275]) used to identify underlying themes of social health and develop a new measure of quantified social health using exploratory and confirmatory factor analysis (CFA), and (2) a validation cohort (31% [86 of 275]) used to test and internally validate the findings on data not used in its development. During the validation process, we found inconsistencies in the correlations of quantified social health with levels of discomfort and capability between the learning and validation cohort that could not be resolved or explained despite various sensitivity analyses. We therefore identified an additional cohort of 356 eligible patients (February 2023 to June 2023) to complete a new extended subset of items directed at financial security and social support (5 items from the initial stage and 11 new items derived from the Interpersonal Support Evaluation List, Financial Well-Being Scale, Multidimensional Scale of Perceived Social Support, Medical Outcomes Study Social Support Survey, and 6-item Social Support Questionnaire, and "I have to work multiple jobs in order to finance my life" was self-created), of whom 95% (338 of 356; 53% women; mean ± SD age 48 ± 16 years; 38% White, 48% Hispanic; 31% Spanish speaking; 47% completed high school or less) completed all items required to perform factor analysis and were included. We repeated factor analysis to identify the underlying themes of social health and then applied item response theory-based graded response modeling to identify the items that were best able to measure differences in social health (high item discrimination) with the lowest possible floor and ceiling effects (proportion of participants with lowest or highest possible score, respectively; a range of different item difficulties). We also assessed the CFA factor loadings (correlation of an individual item with the identified factor) and modification indices (parameters that suggest whether specific changes to the model would improve model fit appreciably). We then iteratively removed items based on low factor loadings (< 0.4, generally regarded as threshold for items to be considered stable) and high modification indices until model fit in CFA was acceptable (root mean square of error approximation [RMSEA] < 0.05). We then assessed local dependencies among the remaining items (strong relationships between items unrelated to the underlying factor) using Yen Q3 and aimed to combine only items with local dependencies of < 0.25. Because we exhausted our set of items, we were not able to address all local dependencies. Among the remaining items, we then repeated CFA to assess model fit (RMSEA) and used Cronbach alpha to assess internal consistency (the extent to which different subsets of the included items would provide the same measurement outcomes). We performed a differential item functioning analysis to assess whether certain items are rated discordantly based on differences in self-reported age, gender, race, or level of education, which can introduce bias. Last, we assessed the correlations of the new quantified social health measure with various self-reported sociodemographic characteristics (external validity) as well as level of discomfort, capability, general health, and mental health (clinical relevance) using bivariate and multivariable linear regression analyses. We identified two factors representing financial security (11 items) and social support (5 items). After removing problematic items based on our prespecified protocol, we selected 5 items to address financial security (including "I am concerned that the money I have or will save won't last") and 4 items to address social support (including "There is a special person who is around when I am in need"). The selected items of the new quantified social health measure (Social Health Scale [SHS]) displayed good model fit in CFA (RMSEA 0.046, confirming adequate factor structure) and good internal consistency (Cronbach α = 0.80 to 0.84), although there were some remaining local dependencies that could not be resolved by removing items because we exhausted our set of items. We found that more disadvantaged quantitative social health was moderately associated with various sociodemographic characteristics (self-reported Black race [regression coefficient (RC) 2.6 (95% confidence interval [CI] 0.29 to 4.9)], divorced [RC 2.5 (95% CI 0.23 to 4.8)], unemployed [RC 1.7 (95% CI 0.023 to 3.4)], uninsured [RC 3.5 (95% CI 0.33 to 6.7)], and earning less than USD 75,000 per year [RC 2.7 (95% CI 0.020 to 5.4) to 6.8 (95% CI 4.3 to 9.3)]), slightly with higher levels of discomfort (RC 0.055 [95% CI 0.16 to 0.093]), slightly with lower levels of capability (RC -0.19 [95% CI -0.34 to -0.035]), slightly with worse general health (RC 0.13 [95% CI 0.069 to 0.18]), moderately with higher levels of unhelpful thoughts (RC 0.17 [95% CI 0.13 to 0.22]), and moderately with greater feelings of distress (RC 0.23 [95% CI 0.19 to 0.28]). A quantitative measure of social health with domains of financial security and social support had acceptable psychometric properties and seems clinically relevant given the associations with levels of discomfort, capability, and general health. It is important to mention that people with disadvantaged social health should not be further disadvantaged by using a quantitative measure of social health to screen or cherry pick in contexts of incentivized or mandated reporting, which could worsen inequities in access and care. Rather, one should consider disadvantaged social health and its associated stressors as one of several previously less considered and potentially modifiable aspects of comprehensive musculoskeletal health. A personalized, quantitative measure of social health would be useful to better capture and understand the role of social health in comprehensive musculoskeletal specialty care. The SHS can be used to measure the distinct contribution of social health to various aspects of musculoskeletal health to inform development of personalized, whole-person care pathways. Clinicians may also use the SHS to identify and monitor patients with disadvantaged social circumstances. This line of inquiry may benefit from additional research including a larger number of items focused on a broader range of social health to further develop the SHS.

Brinkman N ，Broekman M ，Teunis T ，Choi S ，Ring D ，Jayakumar P ... -  《-》

The 2023 Latin America report of the Lancet Countdown on health and climate change: the imperative for health-centred climate-resilient development.

In 2023, a series of climatological and political events unfolded, partly driving forward the global climate and health agenda while simultaneously exposing important disparities and vulnerabilities to climate-related events. On the policy front, a significant step forward was marked by the inaugural Health Day at COP28, acknowledging the profound impacts of climate change on health. However, the first-ever Global Stocktake showed an important gap between the current progress and the targets outlined in the Paris Agreement, underscoring the urgent need for further and decisive action. From a Latin American perspective, some questions arise: How do we achieve the change that is needed? How to address the vulnerabilities to climate change in a region with long-standing social inequities? How do we promote intersectoral collaboration to face a complex problem such as climate change? The debate is still ongoing, and in many instances, it is just starting. The renamed regional centre Lancet Countdown Latin America (previously named Lancet Countdown South America) expanded its geographical scope adding Mexico and five Central American countries: Costa Rica, El Salvador, Guatemala, Honduras, and Panama, as a response to the need for stronger collaboration in a region with significant social disparities, including research capacities and funding. The centre is an independent and multidisciplinary collaboration that tracks the links between health and climate change in Latin America, following the global Lancet Countdown's methodologies and five domains. The Lancet Countdown Latin America work hinges on the commitment of 23 regional academic institutions, United Nations agencies, and 34 researchers who generously contribute their time and expertise. Building from the first report, the 2023 report of the Lancet Countdown Latin America, presents 34 indicators that track the relationship between health and climate change up to 2022, aiming at providing evidence to public decision-making with the purpose of improving the health and wellbeing of Latin American populations and reducing social inequities through climate actions focusing on health. This report shows that Latin American populations continue to observe a growing exposure to changing climatic conditions. A warming trend has been observed across all countries in Latin America, with severe direct impacts. In 2022, people were exposed to ambient temperatures, on average, 0.38 °C higher than in 1986-2005, with Paraguay experiencing the highest anomaly (+1.9 °C), followed by Argentina (+1.2 °C) and Uruguay (+0.9 °C) (indicator 1.1.1). In 2013-2022, infants were exposed to 248% more heatwave days and people over 65 years old were exposed to 271% more heatwave days than in 1986-2005 (indicator 1.1.2). Also, compared to 1991-2000, in 2013-2022, there were 256 and 189 additional annual hours per person, during which ambient heat posed at least moderate and high risk of heat stress during light outdoor physical activity in Latin America, respectively (indicator 1.1.3). Finally, the region had a 140% increase in heat-related mortality from 2000-2009 to 2013-2022 (indicator 1.1.4). Changes in ecosystems have led to an increased risk of wildfires, exposing individuals to very or extremely high fire danger for more extended periods (indicator 1.2.1). Additionally, the transmission potential for dengue by Aedes aegypti mosquitoes has risen by 54% from 1951-1960 to 2013-2022 (indicator 1.3), which aligns with the recent outbreaks and increasing dengue cases observed across Latin America in recent months. Based on the 2023 report of the Lancet Countdown Latin America, there are three key messages that Latin America needs to further explore and advance for a health-centred climate-resilient development. Latin American countries require intersectoral public policies that simultaneously increase climate resilience, reduce social inequities, improve population health, and reduce greenhouse gas (GHG) emissions. The findings show that adaptation policies in Latin America remain weak, with a pressing need for robust vulnerability and adaptation (V&A) assessments to address climate risks effectively. Unfortunately, such assessments are scarce. Up to 2021, Brazil is the only country that has completed and officially reported a V&A to the 2021 Global Survey conducted by the World Health Organization (WHO). Argentina, Guatemala, and Panama have also conducted them, but they have not been reported (indicator 2.1.1). Similarly, efforts in developing and implementing Health National Adaptation Plans (HNAPs) are varied and limited in scope. Brazil, Chile, and Uruguay are the only countries that have an HNAP (indicator 2.1.2). Moreover, self-reported city-level climate change risk assessments are very limited in the region (indicator 2.1.3). The collaboration between meteorological and health sectors remains insufficient, with only Argentina, Brazil, Colombia, and Guatemala self-reporting some level of integration (indicator 2.2.1), hindering comprehensive responses to climate-related health risks in the region. Additionally, despite the urgent need for action, there has been minimal progress in increasing urban greenspaces across the region since 2015, with only Colombia, Nicaragua, and Venezuela showing slight improvements (indicator 2.2.2). Compounding these challenges is the decrease in funding for climate change adaptation projects in Latin America, as evidenced by the 16% drop in funds allocated by the Green Climate Fund (GCF) in 2022 compared to 2021. Alarmingly, none of the funds approved in 2022 were directed toward climate change and health projects, highlighting a critical gap in addressing health-related climate risks (indicator 2.2.3). From a vulnerability perspective, the Mosquito Risk Index (MoRI) indicates an overall decrease in severe mosquito-borne disease risk in the region due to improvements in water, sanitation, and hygiene (WASH) (indicator 2.3.1). Brazil and Paraguay were the only countries that showed an increase in this indicator. It is worth noting that significant temporal variation within and between countries still persists, suggesting inadequate preparedness for climate-related changes. Overall, population health is not solely determined by the health sector, nor are climate policies a sole responsibility of the environmental sector. More and stronger intersectoral collaboration is needed to pave development pathways that consider solid adaptation to climate change, greater reductions of GHG emissions, and that increase social equity and population health. These policies involve sectors such as finance, transport, energy, housing, health, and agriculture, requiring institutional structures and policy instruments that allow long-term intersectoral collaboration. Latin American countries need to accelerate an energy transition that prioritises people's health and wellbeing, reduces energy poverty and air pollution, and maximises health and economic gains. In Latin America, there is a notable disparity in energy transition, with electricity generation from coal increasing by an average of 2.6% from 1991-2000 to 2011-2020, posing a challenge to efforts aimed at phasing out coal (indicator 3.1.1). However, this percentage increase is conservative as it may not include all the fossil fuels for thermoelectric electricity generation, especially during climate-related events and when hydropower is affected (Panel 4). Yet, renewable energy sources have been growing, increasing by an average of 5.7% during the same period. Access to clean fuels for cooking remains a concern, with 46.3% of the rural population in Central America and 23.3% in South America lacking access to clean fuels in 2022 (indicator 3.1.2). It is crucial to highlight the concerning overreliance on fossil fuels, particularly liquefied petroleum gas (LPG), as a primary cooking fuel. A significant majority of Latin American populations, approximately 74.6%, rely on LPG for cooking. Transitioning to cleaner heating and cooking alternatives could also have a health benefit by reducing household air pollution-related mortality. Fossil fuels continue to dominate road transport energy in Latin America, accounting for 96%, although some South American countries are increasing the use of biofuels (indicator 3.1.3). Premature mortality attributable to fossil-fuel-derived PM2.5 has shown varied trends across countries, increasing by 3.9% from 2005 to 2020 across Latin America, which corresponds to 123.5 premature deaths per million people (indicator 3.2.1). The Latin American countries with the highest premature mortality rate attributable to PM2.5 in 2020 were Chile, Peru, Brazil, Colombia, Mexico, and Paraguay. Of the total premature deaths attributable to PM2.5 in 2020, 19.1% was from transport, 12.3% from households, 11.6% from industry, and 11% from agriculture. From emission and capture of GHG perspective, commodity-driven deforestation and expansion of agricultural land remain major contributors to tree cover loss in the region, accounting for around 80% of the total loss (indicator 3.3). Additionally, animal-based food production in Latin America contributes 85% to agricultural CO2 equivalent emissions, with Argentina, Brazil, Panama, Paraguay, and Uruguay ranking highest in per capita emissions (indicator 3.4.1). From a health perspective, in 2020, approximately 870,000 deaths were associated with imbalanced diets, of which 155,000 (18%) were linked to high intake of red and processed meat and dairy products (indicator 3.4.2). Energy transition in Latin America is still in its infancy, and as a result, millions of people are currently exposed to dangerous levels of air pollution and energy poverty (i.e., lack of access to essential energy sources or services). As shown in this report, the levels of air pollution, outdoors and indoors, are a significant problem in the whole region, with marked disparities between urban and rural areas. In 2022, Peru, Chile, Mexico, Guatemala, Colombia, El Salvador, Brazil, Uruguay, Honduras, Panama, and Nicaragua were in the top 100 most polluted countries globally. Transitioning to cleaner sources of energy, phasing out fossil fuels, and promoting better energy efficiency in the industrial and housing sectors are not only climate mitigation measures but also huge health and economic opportunities for more prosperous and healthy societies. Latin American countries need to increase climate finance through permanent fiscal commitments and multilateral development banks to pave climate-resilient development pathways. Climate change poses significant economic costs, with investments in mitigation and adaptation measures progressing slowly. In 2022, economic losses due to weather-related extreme events in Latin America were US$15.6 billion -an amount mainly driven by floods and landslides in Brazil-representing 0.28% of Latin America's Gross Domestic Product (GDP) (indicator 4.1.1). In contrast to high-income countries, most of these losses lack insurance coverage, imposing a substantial financial strain on affected families and governments. Heat-related mortality among individuals aged 65 and older in Latin America reached alarming levels, with losses exceeding the equivalent of the average income of 451,000 people annually (indicator 4.1.2). Moreover, the total potential income loss due to heat-related labour capacity reduction amounted to 1.34% of regional GDP, disproportionately affecting the agriculture and construction sectors (indicator 4.1.3). Additionally, the economic toll of premature mortality from air pollution was substantial, equivalent to a significant portion of regional GDP (0.61%) (indicator 4.1.4). On a positive note, clean energy investments in the region increased in 2022, surpassing fossil fuel investments. However, in 2020, all countries reviewed continued to offer net-negative carbon prices, revealing fossil fuel subsidies totalling US$23 billion. Venezuela had the highest net subsidies relative to current health expenditure (123%), followed by Argentina (10.5%), Bolivia (10.3%), Ecuador (8.3%), and Chile (5.6%) (indicator 4.2.1). Fossil fuel-based energy is today more expensive than renewable energy. Fossil fuel burning drives climate change and damages the environment on which people depend, and air pollution derived from the burning of fossil fuels causes seven million premature deaths each year worldwide, along with a substantial burden of disease. Transitioning to sustainable, zero-emission energy sources, fostering healthier food systems, and expediting adaptation efforts promise not only environmental benefits but also significant economic gains. However, to implement mitigation and adaptation policies that also improve social wellbeing and prosperity, stronger and solid financial systems are needed. Climate finance in Latin American countries is scarce and strongly depends on political cycles, which threatens adequate responses to the current and future challenges. Progress on the climate agenda is lagging behind the urgent pace required. While engagement with the intersection of health and climate change is increasing, government involvement remains inadequate. Newspaper coverage of health and climate change has been on the rise, peaking in 2022, yet the proportion of climate change articles discussing health has declined over time (indicator 5.1). Although there has been significant growth in the number of scientific papers focusing on Latin America, it still represents less than 4% of global publications on the subject (indicator 5.3). And, while health was mentioned by most Latin American countries at the UN General Debate in 2022, only a few addressed the intersection of health and climate change, indicating a lack of awareness at the governmental level (indicator 5.4). The 2023 Lancet Countdown Latin America report underscores the cascading and compounding health impacts of anthropogenic climate change, marked by increased exposure to heatwaves, wildfires, and vector-borne diseases. Specifically, for Latin America, the report emphasises three critical messages: the urgent action to implement intersectoral public policies that enhance climate resilience across the region; the pressing need to prioritise an energy transition that focuses on health co-benefits and wellbeing, and lastly, that need for increasing climate finance by committing to sustained fiscal efforts and engaging with multilateral development banks. By understanding the problems, addressing the gaps, and taking decisive action, Latin America can navigate the challenges of climate change, fostering a more sustainable and resilient future for its population. Spanish and Portuguese translated versions of this Summary can be found in Appendix B and C, respectively. The full translated report in Spanish is available in Appendix D.

Hartinger SM ，Palmeiro-Silva YK ，Llerena-Cayo C ，Blanco-Villafuerte L ，Escobar LE ，Diaz A ，Sarmiento JH ，Lescano AG ，Melo O ，Rojas-Rueda D ，Takahashi B ，Callaghan M ，Chesini F ，Dasgupta S ，Posse CG ，Gouveia N ，Martins de Carvalho A ，Miranda-Chacón Z ，Mohajeri N ，Pantoja C ，Robinson EJZ ，Salas MF ，Santiago R ，Sauma E ，Santos-Vega M ，Scamman D ，Sergeeva M ，Souza de Camargo T ，Sorensen C ，Umaña JD ，Yglesias-González M ，Walawender M ，Buss D ，Romanello M ... -  《-》