**Background:** COVID-19, caused by SARS-CoV-2, has disproportionately affected vulnerable populations, with social determinants of health (SDH) playing a critical role in transmission and outcomes. The Brazilian Amazon, characterized by high social vulnerability, limited healthcare infrastructure, and diverse cultural communities, presents unique challenges. The Xingu Health Region in eastern Pará state includes nine municipalities along the Transamazonian highway and Xingu River, with Altamira as the urban center. This study aimed to investigate the relationship between SDH indicators, incidence, and mortality; identify sociodemographic factors, symptoms, and comorbidities predicting clinical management; and analyze factors associated with lower survival in COVID-19 patients in this pre-vaccination context.
**Methods:** This ecological study analyzed secondary data from the Pará State Health Secretariat (SESPA) database, IBGE, e-Gestor AB, and INPE. Inclusion criteria were SARS-CoV-2 diagnosis by RT-PCR or serological rapid test between March 2020 and March 2021. Cases with missing sociodemographic data were excluded. The final sample comprised 16,138 patients. SDH indicators included population density, percentage of elderly, Gini index, GDP, HDI, PHC coverage, health insurance coverage, physician density, public health expenditure, schooling sub-index, illiteracy, and unemployment rates. Statistical analyses included bivariate correlations (Pearson's r and Spearman's rs), binary logistic regression (univariate and multivariate with VIF for multicollinearity), and Kaplan–Meier survival analysis with log-rank, Breslow, and Tarone–Ware tests. Significance was set at p<0.05.
**Key Results:** Overall COVID-19 incidence was 45.59 per 1,000 inhabitants and mortality was 1.01 per 1,000. The highest incidence was in Vitória do Xingu (90.84 per 1,000) and lowest in Uruará (26.94 per 1,000). Altamira accounted for 41% of cases and 52% of deaths. A total of 359 individuals (2.2%) died. Incidence correlated positively with GDP (rs=0.8000, p=0.013), percentage with health insurance (rs=0.8000, p=0.013), and public health expenditure (rs=0.7667, p=0.021), and negatively with percentage in households with non-masonry/wood walls (r=−0.6764, p=0.040). Mortality correlated positively with health insurance (rs=0.8333, p=0.008) and public health expenditure (rs=0.7333, p=0.030), and negatively with non-masonry/wood walls (r=−0.6812, p=0.040).
Predictors of medical ward admission (vs. home care) included age (aOR 1.05, 95% CI 1.04–1.05, p<0.001), residing in Brasil Novo (aOR 2.02, p=0.001), fever (aOR 1.34, p=0.031), cough (aOR 1.87, p<0.001), emesis (aOR 3.55, p<0.001), dyspnea (aOR 4.01, p<0.001), diabetes (aOR 3.15, p<0.001), heart disease (aOR 2.16, p<0.001), neurological disease (aOR 168, p<0.001), kidney disease (aOR 5.45, p=0.002), and obesity (aOR 13.9, p<0.001). Female sex was protective (aOR 0.68, p=0.001).
Predictors of ICU admission (vs. home care) included age (aOR 1.04, p<0.001), imported case (aOR 6.79, p=0.004), emesis (aOR 6.00, p<0.001), chills (aOR 5.14, p=0.013), dyspnea (aOR 10.2, p<0.001), diabetes (aOR 4.92, p<0.001), heart disease (aOR 3.94, p<0.001), and obesity (aOR 48.0, p<0.001). Female sex (aOR 0.45, p<0.001) and residing in several smaller municipalities were protective.
Predictors of mortality included age (aOR 1.07, 95% CI 1.06–1.08, p<0.001), residing in Anapu (aOR 2.76, p=0.018), fever (aOR 1.54, p=0.022), dyspnea (aOR 3.66, p<0.001), and heart disease (aOR 2.05, p=0.004). Imported case (aOR 0.29, p=0.013), residing in Pacajá (aOR 0.37, p=0.045), sore throat (aOR 0.57, p=0.003), and anosmia (aOR 0.31, p=0.035) were associated with lower mortality. ICU admission (aOR 258, p<0.001) and medical ward admission (aOR 21.1, p<0.001) were strongly associated with death vs. home care. Kaplan–Meier analysis showed only age ≥60 years was significantly associated with lower survival.
**Clinical Implications:** This study demonstrates that in the Xingu Health Region, higher municipal socioeconomic development paradoxically correlated with higher COVID-19 incidence and mortality, likely due to greater connectivity and case detection. Dyspnea, fever, emesis, chills, diabetes, heart disease, obesity, and neurological disease were robust predictors of severe disease requiring ICU care. These findings enable risk stratification at presentation, allowing clinicians to prioritize resources for patients with these features. The protective effect of female sex and certain symptoms (headache, sore throat, anosmia, myalgia) can guide prognostic discussions. The concentration of deaths in Altamira underscores the need to strengthen decentralized healthcare capacity and surveillance in smaller municipalities. Limitations include potential underreporting, missing race/ethnicity data, and the ecological fallacy inherent in group-level analyses.