key: cord-0684571-ceh0nkk9 authors: Ballin, Marcel; Bergman, Jonathan; Kivipelto, Miia; Nordström, Anna; Nordström, Peter title: Excess Mortality After COVID-19 in Swedish Long-Term Care Facilities date: 2021-06-24 journal: J Am Med Dir Assoc DOI: 10.1016/j.jamda.2021.06.010 sha: 27c3686b7b3ca404ca96d9b8fac332c16b58262f doc_id: 684571 cord_uid: ceh0nkk9 OBJECTIVE: To compare 30-day mortality in long-term care facility (LTCF) residents with and without COVID-19 and to investigate the impact of 31 potential risk factors for mortality in COVID-19 cases. DESIGN: Retrospective cohort study. SETTING AND PARTICIPANTS: All residents of LTCFs registered in Senior Alert, a Swedish national database of health examinations in older adults, during 2019-2020. METHODS: We selected residents with confirmed COVID-19 until September 15, 2020, along with time-dependent propensity score–matched controls without COVID-19. Exposures were COVID-19, age, sex, comorbidities, medications, and other patient characteristics. The outcome was all-cause 30-day mortality. RESULTS: A total of 3731 residents (median age 87 years, 64.5% female) with COVID-19 were matched to 3731 controls without COVID-19. Thirty-day mortality was 39.9% in COVID-19 cases and 5.7% in controls [relative risk 7.05, 95% confidence interval (CI) 6.10-8.14]. In COVID-19 cases, the odds ratio (OR) for 30-day mortality was 2.43 (95% CI 1.56-3.79) in cases aged 80-84 years, 2.98 (95% CI 1.92-4.64) in cases aged 85-89 years, and 3.26 (95% CI 2.09-5.06) in cases aged ≥90 years, as compared with cases aged <70 years. Other risk factors for mortality among COVID-19 cases included male sex (OR, 2.56, 95% CI 2.19-3.00), neuropsychological conditions (OR, 2.18; 95% CI 1.75-2.70), impaired walking ability (OR, 1.46, 95% CI 1.19-1.80), urinary and bowel incontinence (OR 1.50, 95% CI 1.22-1.85), diabetes (OR 1.36, 95% CI 1.14-1.62), chronic kidney disease (OR 1.37, 95% CI 1.11-1.69) and previous pneumonia (OR 1.57, 95% CI 1.32-1.85). Nutritional factors, cardiovascular diseases, and antihypertensive medications were not significantly associated with mortality. CONCLUSIONS AND IMPLICATIONS: In Swedish LTCFs, COVID-19 was associated with a large excess in mortality after controlling for a large number of risk factors. Beyond older age and male sex, several prevalent clinical risk factors independently contributed to higher mortality. These findings suggest that reducing transmission of COVID-19 in LTCFs will likely prevent a considerable number of deaths. Finland. 7 In line with this observation, an independent committee of inquiry appointed by the Swedish government concluded that Sweden's strategy to protect older adults living in LTCFs has failed. 8 Thus, as the world continues to battle new waves of the pandemic, it is imperative to identify the most important risk factors for COVID-19 mortality in countries where mortality has been high in LTCFs, such as in Sweden, so that premature deaths can be prevented. Although previous research has identified male sex, older age, and comorbidity as risk factors for severe COVID-19 in the general population, 9e14 data from LTCF residents are lacking. Other than a few small studies, 15e18 only 1 large study has investigated risk factors for 30-day mortality following COVID-19 in LTCF residents. 19 Strong risk factors in this study, apart from older age and male sex, were diabetes, chronic kidney disease, and impaired physical and cognitive function. 19 Yet, the median age was only 79 years, and the study did not include a control group, highlighting that more studies in large, representative populations of LTCF residents are warranted. The aim of the present cohort study was to compare all-cause, 30-day mortality in COVID-19 cases and matched controls living in Swedish LTCFs. An additional aim was to also investigate the impact of 31 potential risk factors for all-cause 30-day mortality in COVID-19 cases. This retrospective cohort study was approved by the Swedish Ethical Review Authority (no. 2020-02552), who waived the informed consent requirement. We considered for inclusion all residents of LTCFs in Sweden who are registered in the Senior Alert database. Launched in 2008, Senior Alert collects data for assessment and prevention of falls, pressure ulcers, malnutrition, and oral health among adults aged 65 years. 20 It is used in hospital wards, home care services, and LTCFs in 90% of Swedish municipalities and regions. 20 An estimated 73% of all Swedish LTCF residents are registered in the database. 21 In the Senior Alert cohort, we identified all COVID-19 cases confirmed in Sweden until mid-September 2020 using the Swedish Public Health Agency's SmiNet database. Reporting confirmed COVID-19 cases to SmiNet is required by law. No information regarding the method of testing was available. COVID-19 cases were excluded from the analysis if they did not have a record in Senior Alert within a year prior to the date of COVID-19 testing or diagnosis (whichever came first or was available). Cases were also excluded if the dates of testing and diagnosis were both unavailable. Persons in the Senior Alert cohort who did not have confirmed COVID-19 (i.e., controls) were included if they had a Senior Alert record in 2019 or 2020 (they were included from the latest record during these years, if there were multiple records). The data were linked using Personal Identification Numbers, which all residents of Sweden have. Statistics Sweden replaced these numbers with pseudo-anonymized identifiers for integrity reasons. The study outcome was all-cause, 30-day mortality, which was obtained from the Swedish Cause of Death Register. 22 Body mass index (BMI, weight in kilograms divided by height in meters squared) was obtained from Senior Alert and was used to define underweight (<18.5), normal weight 18.5-24.99), overweight (25.0-29.99), and obesity (30) . Senior Alert also provided data from 3 validated instruments, which were incorporated into the database upon the advice of an expert panel: Mini Nutritional Assessment, Downton Fall Risk Index, and Modified Norton Scale. 20 The items we selected from these instruments are neuropsychological conditions, known previous falls, walking ability, fluid intake, food intake, incontinence, and general physical condition (Table 1) . 25 This was done by running a Cox regression on all potential risk factors, with COVID-19 as the outcome variable and the date of the Senior Alert record as the time origin. This model was used to calculate a propensity score (the linear predictor), reflecting each individual's probability of contracting COVID-19. Next, each COVID-19 case was matched to the control with the closest propensity score among those who were still alive at the time when the COVID-19 case occurred (time was counted as days since the Senior Alert date). Matching was done sequentially, starting with the first COVID-19 case (in terms of days since cohort entry), then the second, and so on. Controls could only be matched to 1 case, and ties in propensity scores were resolved using random selection. Owing to the relatively small number of COVID-19 cases, we did not match later-diagnosed cases as controls to earlier-diagnosed cases, which is commonly done. 24,25 To ensure close matches, a caliper of 1/10th of the standard deviation of the propensity score was used. 26 The Cox regression model included timevarying covariates for diagnoses and medications, meaning that a new propensity-score was calculated for controls at the time each case occurred. After matching, the baseline date was set to the COVID-19 date in cases and the corresponding date (in days since cohort entry) in controls. In the matched cohort, we calculated the relative risk of all-cause 30-day mortality, with a 95% confidence interval (CI) calculated using the Mantel-Haenszel approach. 27(pp284-286) In COVID-19 cases, we used logistic regression to calculate unadjusted and fully adjusted odds ratios for mortality and 95% CIs. Furthermore, the fully adjusted model was rerun with age as a continuous variable to estimate the absolute risk of death by age and other characteristics. We used fractional polynomials to accommodate potential nonlinearity for the age variable. In the regression models, extreme values for height (<130 cm and >200 cm) and weight (<30 kg and >200 kg) were excluded. For the variables of fluid intake, food intake, and general physical condition, the 2 upper categories were collapsed to 1 category, because the number of participants in the highest category was small (Table 1) . In a sensitivity analysis, we included only COVID-19 cases with a record in Senior Alert within 3 months prior to the date of COVID-19. This restriction was done to examine whether results were affected by the delay between measurements in Senior Alert and the baseline date (COVID-19 test or diagnosis). All analyses were performed using Stata MP version 16.1 for Mac (StataCorp, College Station, TX). Statistical significance was determined as odds ratios with 95% CIs that did not cross 1. 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 There were 216,085 residents in LTCFs registered in Senior Alert (83,519 with a record in 2019 or 2020). Of these 216,085 residents, 5409 were confirmed with COVID-19 from 22 February 2020 to 15 September 2020. Four individuals were excluded because of missing dates of diagnosis and testing. Another 1225 residents were excluded because they did not have a record in Senior Alert within 1 year before the COVID-19 date. Three additional cases were excluded because their death date preceded their date of confirmed COVID-19. Thus, the study cohort comprised 4177 residents with COVID-19 [64.6% female, median age 87 years (interquartile range 81-97)]. Of these individuals, 3732 had complete data and 3731 could be matched to 3731 controls. Baseline characteristics were similar in both unmatched and matched COVID-19 cases and controls (Table 1) . Thirty-day mortality was 39.9% (n ¼ 1487) in COVID-19 cases and 5.7% (n ¼ 211) in controls (relative risk 7.05, 95% CI 6.10-8.14). The association of risk factors with 30-day mortality is presented in Men had 2.5-fold higher odds of 30-day mortality than women after adjustment for other risk factors. The absolute risk of death increased with increasing age; for example, 30-day mortality was approximately 13% in 70-year-old men and 30% in 90-year-old men without other risk factors (Supplementary Figure 11) . Factors related to nutrition (BMI, fluid intake, and food intake) were not associated with 30-day mortality after adjustment, although there was a trend toward increased risk from underweight. Mild and severe neuropsychological conditions (dementia or depression) were both highly prevalent and associated with higher 30-day mortality after adjustment. Compared to those with no conditions, residents with severe conditions had more than twice the odds of 30-day mortality. Neuropsychological conditions were also strongly associated with absolute risk of mortality. For example, a 90-year-old male resident with severe neuropsychological conditions had a 30-day mortality risk of almost 50%, which would have been almost 30% if he had not had neuropsychological conditions (Supplementary Figure 1) . With respect to walking ability, most residents walked unsafely or were unable to walk, which was associated with higher 30-day mortality after adjustment. Compared to those in good general physical condition, residents in poorer condition had higher odds of 30-day mortality after adjustment. A third of residents had urinary and bowel incontinence, which was associated with 1.5-fold higher odds of 30-day mortality after adjustment. Comorbidities associated with higher adjusted odds of 30-day mortality included diabetes, renal failure or chronic kidney disease, and pneumonia. Importantly, diabetes and previous pneumonia were also highly prevalent. Cancer, chronic obstructive pulmonary diseases, and antihypertensives (other than diuretics) were not associated with 30-day mortality before or after adjustment. Cardiovascular diseases, antithrombotic medication, and diuretics were only associated with higher 30-day mortality before adjustment. The sensitivity analysis comprised 1421 residents registered in Senior Alert within 3 months of confirmed COVID-19 (median 47 days, interquartile range 27-68). Of these, 566 died within 30 days (39.9%). Overall, this analysis confirmed the results of the main analysis (Supplementary Table 2 ). This study showed that 30-day mortality was 40% in Swedish LTCF residents with COVID-19, which was 7 times higher than in matched controls without COVID-19. Beyond older age and male sex, independent risk factors for higher mortality were neuropsychological conditions, impaired walking ability, incontinence, diabetes, chronic kidney disease, and previous pneumonia. These risk factors, most of which are not modifiable, were highly prevalent, and associated with a high absolute risk of death, altogether emphasizing the importance of preventing COVID-19 transmission to LTCFs. The 40% mortality rate in our study is almost twice as high as in a US study of more than 5000 nursing home residents. 19 This difference likely reflects that our study cohort was older. Smaller studies, conducted in age groups similar to ours, showed more comparable mortality rates. 15, 28, 29 A limitation of all these studies is that they lacked a control group, impeding assessment of excess mortality. In this sense, our results add important evidence regarding the profound dangers of COVID-19 in LTCFs, as illustrated by the 7-fold higher mortality. Although the reason for the high mortality is likely multifactorial and complex, the disease indisputably has a tremendous significance from a public health perspective, affecting older adults, especially those living in LTCFs, disproportionally. In support, a recent study showed that LTCF residents had 4 times higher risk of COVID-19 mortality, compared with community-dwelling older adults. 30 Further, another study showed that COVID-19 is more dangerous for older adults compared to seasonal influenza, especially for older adults with certain comorbidities, being associated with a 5-fold higher risk of death. 31 Our study provides additional evidence that COVID-19 mortality is high also in older adults without other risk factors. Altogether, the findings from our study suggest that COVID-19 has caused a large number of premature deaths in Swedish LTCFs. In our study, older age, male sex, and neuropsychological conditions were among the most important risk factors for 30-day mortality in LTCF residents with COVID-19. Although these risk factors are known from previous studies, 15, 16, 19, 29 less is known about their additive effects. Therefore, we also examined how the absolute risk of death varied depending on these 3 risk factors. For example, a 90year-old male resident with severe neuropsychological conditions had a 30-day mortality risk of around 50%, which would have been 30% if he had not had neuropsychological conditions. In women, the corresponding difference was around 25% vs 15%. These large absolute differences strengthen the clinical importance of these 3 risk factors, and pinpoints groups that are especially critical to protect against being infected in LTCFs. Two other common patient characteristics that were associated with higher mortality were impaired walking ability and urine and bowel incontinence. Previous studies found physical function and frailty to be risk factors for 30-day mortality after COVID-19 in LTCFs 15, 19 and in-hospital mortality in older adults. 32 In one study, bowel incontinence was a risk factor for COVID-19 diagnosis. 17 Thus, our study shows that in a large cohort of LTCF residents, easy-to-assess characteristics such as walking ability and incontinence are prevalent and independent risk factors for mortality after COVID-19. In contrast, no association was found between obesity and mortality. Although obesity is a well-known risk factor for developing severe COVID-19 in the general population, 14 studies in older people have shown conflicting results. 33, 34 The lack of association in our study may be related to the well-known obesity paradox in very old people, 35 for whom body-mass-index is a poor indicator of body composition and body fat distribution. 36 It has also been hypothesized that malnutrition could be an important risk factor, 37,38 but we did not find an association between food intake and mortality after adjustment for other risk factors, as in a previous study. 18 However, there was a trend toward an increased risk of mortality in those with the lowest BMI. Although this did not reach statistical significance, likely because of the small number of people in that BMI category, it cannot be ruled out that underweight is a risk factor for COVID-19 mortality in LTCF residents. Having diabetes or renal failure or chronic kidney disease was both common and associated with increased risk of 30-day mortality. Both these conditions have previously been identified as risk factors for mortality following COVID-19 in LTCF residents. 19 Also, history of pneumonia was common and a strong risk factor for 30-day mortality. Although we are not aware of any other studies that have investigated pneumonia as a risk factor in LTCF residents, it was recently shown that previous pneumonia is a risk factor for COVID-19 diagnosis, hospitalization, and subsequent all-cause mortality in the general Swedish population. 39 Hypothetically, previous pneumonia could be a marker of impaired immune function that increases one's susceptibility for severe COVID-19 infection. In our study, antihypertensives were not associated with mortality after COVID-19. This extends the results of observational studies showing that hypertension is not a risk factor in LTCF residents 19, 29 and is supported by randomized studies showing that continuation of antihypertensive treatment did not increase the risk of severe outcomes, as compared to discontinuation, in patients hospitalized with COVID-19. 40, 41 Similarly, many other common diseases or medications were not associated with mortality in this study, including cardiovascular disease, antithrombotics, pulmonary disease, and cancer. An explanation for these findings could be that many prevalent diagnoses have little impact on mortality risk in very frail older people who have lived to an old age. It should also be noted that because different conditions are likely less often diagnosed in LTCFs, and primary care diagnoses care are not captured in the NPR, the sensitivity to capture different diagnoses is likely lower than in community-dwelling individuals. Regardless, our results are similar to previous studies of nursing home residents, 15, 19, 29 geriatric patients, 32 and veterans. 42 This study has several important strengths. To our knowledge, this is the first study to evaluate 30-day mortality following COVID-19 in LTCFs using a control group. The study cohort included a large representative population of LTCF residents from the whole of Sweden, who are of particular importance to study given that they have experienced the highest mortality rates from COVID-19. Moreover, more than 30 potential risk factors and clinical patient characteristics were available, and these were investigated through a comprehensive set of analyses, increasing the credibility of our findings. Some limitations of this study should also be considered. First, the data obtained from Senior Alert may not be completely accurate for the time of COVID-19 infection owing to the lag time between assessment in Senior Alert and baseline (COVID-19), although a sensitivity analysis suggested that this did not bias associations. Second, because the study cohort was restricted to residents in LTCFs with a record in Senior Alert in the past year, generalizability to all LTCF residents in Sweden may in theory be limited. However, our data captured 5409 cases compared to the 7143 cases confirmed in LTCFs in Sweden until mid-September according to official data, 43 meaning that the coverage in our study was high. Third, the accuracy regarding identification of certain risk factors may be limited. For example, neuropsychological conditions comprised both dementia and depression, although these are clearly different conditions. Yet, using this item, we observed an OR of similar magnitude to that shown in a previous study where cognitive function was assessed using the Minimum Data Set. 19 Moreover, although we had access to a wide array of potential risk factors, we lacked data on symptoms at COVID-19 presentation, which have previously been associated with mortality following COVID-19 in LTCF residents. 19 Fifth, data on LTCF characteristics were lacking, for example data on staffing and structure, which could have influenced the transmission and mortality of COVID-19. Sixth, the results are not necessarily generalizable to other countries. Finally, although COVID-19 cases and controls were matched, there may be unmeasured differences between the groups that may partly have contributed to the higher mortality in cases. In summary, 30-day mortality was 7 times higher in Swedish LTCF residents with COVID-19 than in matched controls, suggesting that the excess mortality is due to the COVID-19 infection itself and not older age or poorer health status. In addition, beyond older age and male sex, some diagnoses and simple measures of health status predict short-term mortality. Because large-scale community transmission has been deemed one of the primary explanations for the high COVID-19 mortality rates in Swedish LTCFs, 8 our findings emphasize that reducing transmission of COVID-19 to LTCFs, would likely prevent a considerable number of deaths in this frail group of older individuals. Measures taken by Swedish authorities during the first wave of the COVID-19 pandemic. An in-depth review of Sweden's COVID-19 strategy, including a detailed timeline of key events and adopted measures, during the first wave of the pandemic is available from Ludvigsson JF. The first eight months of Sweden's COVID-19 strategy and the key actions and actors that were involved. Acta Paediatr 2020; 109:2459e2471. 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 043 044 045 046 047 048 049 050 051 052 053 054 055 056 057 058 059 060 061 062 063 064 065 066 067 068 069 070 071 072 073 074 075 076 077 078 079 080 081 082 083 084 085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360 1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392 1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408 1409 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488 1489 1490 1491 1492 1493 1494 1495 1496 1497 1498 1499 1500 1501 1502 1503 1504 1505 1506 1507 1508 1509 1510 1511 1512 1513 1514 1515 1516 1517 1518 1519 1520 1521 1522 1523 1524 1525 1526 1527 1528 1529 1530 1531 1532 1533 1534 1535 1536 1537 1538 1539 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 1606 1607 1608 1609 1610 1611 1612 1613 1614 1615 1616 1617 1618 1619 1620 1621 1622 1623 1624 1625 1626 1627 1628 1629 1630 1631 1632 1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 1644 1645 1646 1647 1648 1649 1650 1651 1652 1653 1654 1655 1656 1657 1658 1659 1660 1661 1662 1663 1664 1665 1666 1667 1668 1669 1670 Infection prevention and control guidance for long-term care facilities in the context of COVID-19: Interim guidance High impact of COVID-19 in long-term care facilities, suggestion for monitoring in the EU/EEA The impact of COVID-19 pandemic on long-term care facilities worldwide: An overview on international issues A comparison of COVID-19 mortality rates among long-term care residents in 12 OECD countries COVID-19 deaths in long-term care facilities: A critical piece of the pandemic puzzle Statistik om smittade och avlidna med covid-19 bland äldre efter boendeform Underlagsrapport till SOU 2020:80 Äldreomsorgen under pandemin Äldreomsorgen under pandemin (SOU 2020:80). 2020. Available at Features of 20 133 UK patients in hospital with covid-19 using the ISARIC WHO Clinical Characterisation Protocol: prospective observational cohort study Risk factors for intensive care unit admission and in-hospital mortality among hospitalized adults identified through the U.S. Coronavirus Disease 2019 (COVID-19)-Associated Hospitalization Surveillance Network (COVID-NET) Factors associated with hospital admission and critical illness among 5279 people with coronavirus disease Factors associated with COVID-19-related death using OpenSAFELY Characteristics and predictors of hospitalization and death in the first 11 122 cases with a positive RT-PCR test for SARS-CoV-2 in Denmark: A nationwide cohort Obesity in patients with COVID-19: A systematic review and meta-analysis Clinical characteristics, frailty, and mortality of residents with COVID-19 in nursing homes of a region of Madrid SARS-CoV-2 infection, clinical features and outcome of COVID-19 in United Kingdom nursing homes Risk factors, presentation, and course of coronavirus disease 2019 in a large, academic long-term care facility Clinical features and medical care factors associated with mortality in French nursing homes during the COVID-19 outbreak Risk factors associated with allcause 30-day mortality in nursing home residents with COVID-19 Senior alert: A quality registry to support a standardized, structured, and systematic preventive care process for older adults Senior Alert. Årsrapport Balanced risk set matching An introduction to propensity score methods for reducing the effects of confounding in observational studies Modern Epidemiology Clinical presentation, course, and risk factors associated with mortality in a severe outbreak of COVID-19 in Mortality and the use of antithrombotic therapies among nursing home residents with COVID-19 Residential context and COVID-19 mortality among adults aged 70 years and older in Stockholm: A population-based, observational study using individual-level data Comparative evaluation of clinical manifestations and risk of death in patients admitted to hospital with COVID-19 and seasonal influenza: Cohort study Age, frailty, and comorbidity as prognostic factors for short-term outcomes in patients with coronavirus disease 2019 in geriatric care BMI and future risk for COVID-19 infection and death across sex, age and ethnicity: Preliminary findings from UK biobank Signs, symptoms, and comorbidities associated with poor outcomes among residents of a skilled nursing facility with SARS-CoV-2 infectiondKing County Obesity paradox in aging: From prevalence to pathophysiology Age-related changes in total and regional fat distribution Could nutritional and functional status serve as prognostic factors for COVID-19 in the elderly? Prevalence of malnutrition and analysis of related factors in elderly patients with COVID-19 in Wuhan, China Risk factors for COVID-19 diagnosis, hospitalization and subsequent all-cause mortality in Sweden: A nationwide study Effect of discontinuing vs continuing angiotensin-converting enzyme inhibitors and angiotensin II receptor blockers on days alive and out of the hospital in patients admitted with COVID-19: A randomized clinical trial Continuation versus discontinuation of renin-angiotensin system inhibitors in patients admitted to hospital with COVID-19: A prospective, randomised, open-label trial Risk factors for hospitalization, mechanical ventilation, or death among 10131 US Veterans with SARS-CoV-2 infection Veckorapport om covid-19, vecka 37 Diabetes and absolute risk of 30-day mortality in men without other risk factors. Supplementary Fig. 10. Diabetes and absolute risk of 30-day mortality in women without other risk factors The authors received funding used for salaries from Foundation Stockholms Sjukhem (MK), Academy of Finland (MK), Läkarsällskapet (MK), and the Supplementary Fig. 11 . Age and absolute risk of 30-day mortality in men without other risk factors.