Incidence and risk factors for AIDS-related mortality in HIV patients in China: a cross-sectional study

  • Hui Zheng1,

    Affiliated with

    • Lu Wang2,

      Affiliated with

      • Peng Huang1,

        Affiliated with

        • Jessie Norris2,

          Affiliated with

          • Qing Wang1,

            Affiliated with

            • Wei Guo2,

              Affiliated with

              • Zhihang Peng1Email author,

                Affiliated with

                • Rongbin Yu1 and

                  Affiliated with

                  • Ning Wang2Email author

                    Affiliated with

                    BMC Public Health201414:831

                    DOI: 10.1186/1471-2458-14-831

                    Received: 24 February 2014

                    Accepted: 3 July 2014

                    Published: 11 August 2014

                    Abstract

                    Background

                    To estimate the incidence and risk factors for mortality in HIV-1-infected patients in China.

                    Methods

                    Information on AIDS-related deaths was collected from the Chinese Center for Disease Control and Prevention’s Disease Surveillance Information Reporting System and AIDS Prevention and Control Information System.

                    Results

                    A total of 379,348 HIV cases were recorded in the databases from 2006. Among those, 138,288 patients were reported as having developed AIDS and 72,616 (19%) died of AIDS after data was extracted from the databases in January 2011. Mortality was higher among those patients aged 50 years old or older (AOR: 3.41, CI: 1.47-7.91) who had been infected by intravenous drug use (AOR: 1.65, CI: 1.28-2.14) or blood transfusion/donation (AOR: 2.18: 1.18-3.99). Compared to patients who had not initiated highly active antiretroviral therapy (HAART), those who had initiated HAART were more likely to have a long interval of time between infection confirmation and AIDS-related death.

                    Conclusions

                    The effective reduction of AIDS mortality could be improved through timely treatment.

                    Keywords

                    HIV/AIDS Highly active antiretroviral treatment Mortality China

                    Background

                    There are currently an estimated 780,000 people living with HIV in China; at the end of 2011, the cumulative number of reported deaths since the beginning of the HIV epidemic was 93,000 [1]. A report released by the Ministry of Health in 2009 showed that HIV/AIDS had become the leading cause of death from infectious disease in China [2].

                    The expansion of highly active antiretroviral therapy (HAART) in recent years has offered a hope of preventing thousands of deaths [312]. HAART [13] use has notably reduced HIV-related morbidity and mortality in both industrialized and low-income settings since 1996 [4, 1417]. HAART has also contributed additional benefits such as prolonged disease-free survival, durable HIV virologic suppression, immunologic (CD4 cell) repletion, and reductions in hospitalization rates [14, 18, 19]. As a result of the Chinese government scaling up the National Free Antiretroviral Treatment Programme (NFATP) in 2003, approximately 10,000 people in China were receiving HAART by the end of 2011 [20].

                    Most previous reports in China have focused on the effects of HAART on clinical and immunologic outcomes compared to those observed in resource-rich settings [21]. Previous studies have already identified that patients starting HAART with low CD4 cell counts have fewer opportunistic infections such as tuberculosis, acute sepsis, cryptococcosis, and toxoplasmosis [2225]. However, in-depth analysis and systematic research into causes of death among people with HIV/AIDS in China is lacking.

                    This study presents data from the China Disease Prevention and Control Information System on changes in AIDS mortality through December 31, 2010. It also determines the incidence and related risk factors of mortality following the widespread availability of HAART in China after 2003.

                    Methods

                    Data collection

                    Data on HIV/AIDS patients were collected from the China Disease Prevention and Control Information System. For mortality cases, detailed information was downloaded from the Disease Surveillance Information Reporting System and AIDS Prevention and Control Information System. These databases contain information on patient demographic characteristics, reported provinces of residence, survival time (from diagnosis to death), cause of death, and HAART use. If one case was contained in both databases, we selected the information on the case from AIDS Prevention and Control Information System. All data were assessed from January 1, 2006 to December 31, 2010. HAART was scaled up nationwide in 2003, with data prospectively included in the database after 2004. However, the data collected from 2004 to 2005 covered only part of the provinces, and nationwide collection was not yet initiated; for this reason, the mortality data did not start until 2006.

                    All records in the databases had undergone quality control, and cases were confirmed in a laboratory. Data were recorded solely for individuals from mainland China, excluding people from Hong Kong, Macao and Taiwan. Non-Chinese citizens were also excluded from our study.

                    A second analysis was conducted on mortality cases from January 1, 2011 to July 31, 2011. The effects of treatment on death were analyzed by studying the number of days between the date of death and the date of either the first CD4 test or the date of AIDS diagnosis if CD4 test results were unavailable.

                    Ethics statement

                    This study, including design, recruitment, consent, and assessment procedures, was reviewed and approved by the Institutional Review Board of Nanjing Medical University.

                    Cause of death

                    Causes of death for cases reported in the Data Information Management System were verified. Among the 32,562 cases with a cause of death listed as “other reasons”, some were confirmed as AIDS-related death by verifying the information filed. When the cause of death was listed as tuberculosis, opportunistic infections (OI), pneumocystis pneumonia (PCP), or AIDS-related disease, the cause of death was reclassified as AIDS. Taking into account that tuberculosis is prevalent in China, those deaths were removed from the analysis of AIDS-related deaths if the patient’s last CD4 did not fall below 200/uL, to avoid overestimating the number of AIDS-related deaths.

                    Identify key variables

                    A late diagnosis of AIDS-related death was defined as a diagnosis of AIDS (CD4 < 200 cells/μL or AIDS-related clinical symptoms) [26] where death occurred within one year after an HIV diagnosis. All patients were eligible for free treatment if their HIV was in WHO clinical stage 3 or 4, or if they had a CD4 count < 200 cells/μL or <350 cells/μL after 2008. Untreated deaths were defined as patients who died without initiating treatment. Mortality cases with antiviral treatment information in the treatment database were matched to the same cases in the mortality database. The deaths of patients who were not listed in the treatment registry were considered untreated deaths.

                    Statistical analysis

                    SPSS (version 20.0), Stata (version 12.0) and Excel (version 2010) were used for data analysis. Descriptive analyses were conducted to describe mortality cases’ characteristics, including mean (±SD), median (interquartile range, IQR), and frequencies (%). A logistic regression was applied to determine the risk factors of HIV/AIDS mortality. Kaplan-Meier survival curves were used to estimate the probability of death and the median time to death after HAART initiation. The log-rank test was used to compare the median time to death between the four groups. Statistical significance was assessed at the 0.05 level, and all hypothesis tests were two-sided.

                    Results

                    General characteristics

                    In our study, annual reported deaths from HIV/AIDS from 2006 to 2010 comprised 1809; 5544; 9748; 12,287; and 18,987 cases, respectively. The estimated annual number of new infections was 70,000; 50,000; 48,000; and 48,000 in 2005, 2007, 2009, and 2011, respectively. Based on our data, we estimated the annual mortality rates as being 2.5% in 2007, 3.5% in 2008 and 5.0% in 2010. The actual annual number of HIV/AIDS deaths after verifying cause of death from 2006 to 2010 was 7,013; 9,298; 11,921; 13,832; and 13,981, respectively, revealing a slower increase than in the data reported. Annual cases of AIDS-related deaths among actual annual cases from 2006 to 2010 were 2764; 3575; 4877; and 5675; and 5,793, respectively, showing a relatively slight upward trend.

                    The total number of deaths in male patients was 54,904. Among all patients, death was more common among those who were 20–49 years of age (75.3%). The median age of death was 38.5 years old, 38.4 years for males and 38.7 years for females. Among self-reported sources of transmission, the proportions of total deaths from infections through heterosexual transmission, injection drug use (IDU), blood transfusion/donation, homosexual transmission and others were 31.7%, 28.4%, 24.0%, 0.8% and 1.1%, respectively. One-fifth (19.7%) of HIV-infected patients among cumulative deaths reported the use of HAART (Table  1).
                    Table 1

                    Demographics of the actual HIV/AIDS deaths and their associated risk factors from 2006 to 2010

                    Year

                    2006

                    2007

                    2008

                    2009

                    2010

                    Total No. (%)

                    OR (95% CI)

                    AOR (95% CI)

                    Death

                    7013

                    9298

                    11921

                    13832

                    13981

                    72616

                      

                    Sex

                            

                    Male

                    5167

                    6974

                    9013

                    10688

                    10874

                    54904 (75.6)

                    1.0

                    1.0

                    Female

                    1846

                    2324

                    2908

                    3144

                    3107

                    17712 (24.4)

                    1.51 (1.22-1.87)

                    1.02 (0.79-1.32)

                    Age (median)

                    37

                    38

                    39

                    40

                    41

                    38.5

                    1.05 (1.04-1.06)

                    1.03 (1.01-1.06)

                    0-20

                    170

                    226

                    210

                    231

                    214

                    1624 (2.3)

                    1.0

                    1.0

                    20-29

                    1199

                    1455

                    1632

                    1667

                    1589

                    11153 (15.4)

                    0.85 (0.41-1.78)

                    0.89 (0.40-1.97)

                    30-39

                    2817

                    3690

                    4526

                    4870

                    4597

                    27183 (37.4)

                    1.66 (0.81-3.41)

                    1.34 (0.61-3.00)

                    40-49

                    1582

                    2028

                    2755

                    3195

                    3466

                    16365 (22.5)

                    2.02 (0.97-4.21)

                    1.39 (0.61-3.19)

                    >50

                    1244

                    1899

                    2798

                    3869

                    4115

                    16188 (22.3)

                    5.33 (2.55-11.15)

                    3.41 (1.47-7.91)

                    Transmission routes

                            

                    Heterosexual

                    1423

                    2509

                    4264

                    5974

                    6796

                    23009 (31.7)

                    1.0

                    1.0

                    IDU

                    1872

                    2729

                    3402

                    3784

                    3942

                    20616 (28.4)

                    1.36 (1.11-1.68)

                    1.65 (1.28-2.14)

                    Blood transfusion/donation

                    2288

                    2178

                    2154

                    1513

                    1495

                    17487 (24.0)

                    3.34 (1.98-5.61)

                    2.18 (1.18-3.99)

                    Homosexual

                    22

                    48

                    96

                    151

                    275

                    615 (0.8)

                    0.18 (0.14-0.25)

                    0.33 (0.23-0.47)

                    Others

                    94

                    133

                    113

                    146

                    127

                    792 (1.1)

                    0.77 (0.50-1.20)

                    0.87 (0.48-1.56)

                    Marital status

                            

                    Unmarried

                    1386

                    1997

                    2479

                    2843

                    2935

                    14547 (20.0)

                    1.0

                    1.0

                    Married

                    4260

                    5395

                    7093

                    8071

                    7938

                    42239 (58.2)

                    2.43 (1.99-2.97)

                    1.16 (0.89-1.50)

                    Divorced or widowed

                    743

                    1171

                    1536

                    2128

                    2378

                    9109 (12.5)

                    2.53 (1.95-3.28)

                    1.12 (0.81-1.56)

                    Received HAART

                            

                    No

                    5380

                    7423

                    8945

                    10856

                    10866

                    58318 (80.3)

                    1.0

                    1.0

                    Yes

                    1633

                    1875

                    2513

                    2976

                    3115

                    14298 (19.7)

                    0.41 (0.32-0.51)

                    0.11 (0.08-0.14)

                    The three risk factors most strongly related to mortality were age, not having received HAART and having multiple transmission routes. IDUs (AOR, adjusted odds ratio: 1.65, CI, confidence interval: 1.28-2.14) and blood transfusion/donations (AOR: 2.18, CI: 1.18-3.99) were significantly more likely to having a high mortality compared to those infected through heterosexual transmission; however, the statistical results of individuals infected by homosexual transmission (AOR: 0.33, CI: 0.23-0.47) were just the opposite. Patients aged 50 years old or older (AOR: 3.41, CI: 1.47-7.91) and those who had not received HAART were also significantly likely to have a high mortality. Although sex and marital status were not significantly associated with mortality, female gender and a status of married or divorced had a high risk of mortality in the unadjusted analysis, but these factors were not included in the adjusted analysis (Table  1).

                    AIDS deaths were reported in the 31 mainland provinces by the end of 2010. The top six provinces with the highest cumulative number of reported HIV/AIDS cases, as well as the highest cumulative number of reported deaths, were Yunnan, Guangxi, Henan, Sichuan, Xinjiang and Guangdong. The top five provinces with the highest proportion of cumulative reported deaths accounting for the cumulative number of reported HIV/AIDS cases were Shanxi, Hubei, Henan, Hebei, and Anhui. The proportion of HIV/AIDS-related deaths was low in provinces such as Beijing, Shanghai, Tianjin, and Zhejiang (Table  2).
                    Table 2

                    The provincial statistics of the cumulative reported cases and the cumulative AIDS-related death cases with HAART

                    Province

                    Cumulative reported cases

                    Cumulative death cases

                    Proportion of deaths (%)

                    Number

                    Accepted HARRT

                    Percentage (%)

                    Number

                    Accepted HARRT

                    Percentage (%)

                    Anhui

                    6590

                    3564

                    54.1

                    1730

                    446

                    25.8

                    26.3

                    Beijing

                    5259

                    1053

                    20

                    148

                    25

                    16.9

                    2.8

                    Fujian

                    2649

                    710

                    26.8

                    568

                    55

                    9.7

                    21.4

                    Gansu

                    893

                    234

                    26.2

                    163

                    19

                    11.7

                    18.3

                    Guangxi

                    63127

                    16560

                    26.2

                    1359

                    1329

                    9.8

                    21.5

                    Guangdong

                    28534

                    4471

                    15.7

                    5829

                    437

                    7.5

                    20.4

                    Guizhou

                    10290

                    1351

                    13.1

                    1826

                    143

                    7.8

                    17.7

                    Hainan

                    975

                    98

                    10.1

                    292

                    9

                    3.1

                    29.9

                    Hebei

                    1913

                    754

                    39.4

                    607

                    110

                    18.1

                    31.7

                    Henan

                    49325

                    32282

                    65.4

                    1467

                    7471

                    50.9

                    29.7

                    Heilongjiang

                    1571

                    412

                    26.2

                    228

                    34

                    14.9

                    14.5

                    Hubei

                    6613

                    2689

                    40.7

                    2039

                    439

                    21.5

                    30.8

                    Hunan

                    10794

                    2736

                    25.3

                    2831

                    374

                    13.2

                    26.2

                    Jilin

                    1504

                    479

                    31.8

                    387

                    66

                    17.1

                    25.7

                    Jiangsu

                    4084

                    1337

                    32.7

                    643

                    102

                    15.9

                    15.7

                    Jiangxi

                    2556

                    983

                    38.5

                    822

                    169

                    20.6

                    32.2

                    Liaoning

                    2785

                    585

                    21

                    347

                    49

                    14.1

                    12.5

                    Neimengu

                    621

                    119

                    19.2

                    97

                    20

                    20.6

                    15.6

                    Ningxia

                    370

                    77

                    20.8

                    44

                    10

                    22.7

                    11.9

                    Qinghai

                    317

                    98

                    30.9

                    45

                    11

                    24.4

                    14.2

                    Shandong

                    2272

                    754

                    33.2

                    547

                    87

                    15.9

                    24.1

                    Shanxi

                    3055

                    1180

                    38.6

                    1002

                    158

                    15.8

                    32.8

                    Shanxi

                    1569

                    460

                    29.3

                    309

                    72

                    23.3

                    19.7

                    Shanghai

                    5260

                    1154

                    21.9

                    239

                    48

                    20.1

                    4.5

                    Sichuan

                    38356

                    5041

                    13.1

                    5485

                    440

                    8

                    14.3

                    Tianjin

                    885

                    237

                    26.8

                    99

                    22

                    22.2

                    11.2

                    Tibet

                    126

                    8

                    6.3

                    10

                    1

                    10

                    7.9

                    Xinjiang

                    33519

                    4119

                    12.3

                    4107

                    379

                    9.2

                    12.3

                    Yunnan

                    79433

                    18953

                    23.9

                    1222

                    1537

                    12.6

                    15.4

                    Zhejiang

                    4713

                    1695

                    36

                    494

                    88

                    17.8

                    10.5

                    Chongqing

                    9390

                    1603

                    17.1

                    1179

                    148

                    12.6

                    12.6

                    Nationwide

                    379348

                    105796

                    27.9

                    72616

                    14298

                    19.7

                    19.1

                    Characteristics according to time since HIV diagnosis and HAART

                    Among the 72,616 deaths, the median time between diagnosis and death was 0.7 years (IQR: 0.1, 2.6). The median time of HIV infection between diagnosis and death was 0.9 years (IQR: 0.1, 2.9). Simultaneously, the median time of AIDS infection between diagnosis and death was 0.6 years (IQR: 0.1, 2.4).

                    The median time between diagnosis and death for patients receiving treatment was 1.6 years (IQR: 0.44, 3.62), while the median time between diagnosis and death for untreated patients was 0.5 years (IQR: 0.1, 2.2).

                    Table  3 presents and compares the interval between diagnosis and death according to HAART initiation status. The different day ranges between diagnosis and death have different numbers of death, more than 180 days was the most, less than 30 days, 30 to 60 days, 60 to 90 days, 90 to 180 days was 15615, 5543, 4002, 7164, respectively. Among all deaths by the end of 2010, 80.3% (58318/72616) of patients who died did not receive HAART and 75.0% (43732/58318) of those had no CD4 test result. For those patients with CD4 test results, 62.8% (9167/14586) had a CD4 count below 200 at their first lab test, and 78.9% (11643/14586) had a CD4 count below 350 at their first lab test. Compared to patients who had not initiated HAART, those who had initiated HAART were more likely to have a long interval of time between diagnosis and death (χ 2  = 3621.19, p < 0.001).
                    Table 3

                    HAART and CD4 test results of the cumulative HIV/AIDS deaths by the end of 2010

                    The interval between infection confirmation and deaths (day)

                    Deaths

                    Initiated HAART No. (%)

                    Not receiving HAART

                    First laboratory CD4 count

                    No CD4 test result No. (%)

                     

                    ≤200

                    200-350

                    >350

                    Total No. (%)

                    <30

                    15615

                    610 (3.9)

                    1346

                    77

                    61

                    1484 (9.5)

                    13521 (86.6)

                    30-

                    5543

                    788 (14.2)

                    1014

                    77

                    54

                    1145 (20.7)

                    3610 (65.1)

                    60-

                    4002

                    832 (20.8)

                    831

                    52

                    60

                    943 (23.6)

                    2227 (55.6)

                    90-

                    7164

                    1573 (22.0)

                    1400

                    161

                    170

                    1731 (24.2)

                    3860 (53.9)

                    ≥180

                    40292

                    10495 (26.0)

                    4576

                    2109

                    2598

                    9283 (23.0)

                    20514 (50.9)

                    Total

                    72616

                    14298 (19.7)

                    9167

                    2476

                    2943

                    14586 (20.0)

                    43732 (60.2)

                    According to the AIDS Network Reporting System, by the end of 2010, 56.1% (40746/72616) of the cumulative number of patients who died were late diagnoses. Of those who died after receiving a late diagnosis, 73.5% were male, 64.6% were married, and 74.5% were ethnically Han. Transmission categories for these deaths included heterosexual transmission (43.4%), IDU (15.9%), blood donation (14.4%), and blood transfusion (8.3%). Most (61.9%) patients were first diagnosed in their counties of residence.

                    Among the patients who died who had never initiated HAART, 77.4% were male, 55.5% were married, and 67.3% were ethnically Han. Transmission categories included heterosexual transmission (33.2%), IDU (32.0%), blood donation (11.3%) and blood transfusion (5.4%). Most (60.9%) patients were first diagnosed in their counties of residence (Table  4).
                    Table 4

                    Demographics of late diagnosis of HIV deaths and untreated deaths by the end of 2010

                     

                    Late diagnosis of deaths

                    Untreated deaths cases

                    No. (%)

                    No. (%)

                    Sex

                      

                    Male

                    29954 (73.5)

                    45162 (77.4)

                    Female

                    10792 (26.5)

                    13155 (22.6)

                    Age (years)

                      

                    0-9

                    1062 (2.6)

                    634 (1.1)

                    10-19

                    /

                    533 (0.9)

                    20-29

                    5556 (13.6)

                    6339 (10.9)

                    30-39

                    13709 (33.7)

                    13795 (23.7)

                    40-49

                    9360 (23.0)

                    8801 (15.1)

                    50

                    10981 (27.0)

                    10567 (18.1)

                    Marital status

                      

                    Married

                    26336 (64.6)

                    32390 (55.5)

                    Unmarried

                    7019 (17.2)

                    12518 (21.5)

                    Divorced or widowed

                    5222 (12.8)

                    6913 (11.9)

                    Ethnicity

                      

                    Han

                    30373 (74.5)

                    39220 (67.3)

                    Zhuang/Wei/Yi/Dai

                    5352 (13.1)

                    10078 (17.3)

                    Other ethnicity

                    /

                    /

                    Infection Routes

                      

                    Heterosexual

                    17693 (43.4)

                    19387 (33.2)

                    IDU

                    6462 (15.9)

                    18666 (32.0)

                    Blood Donation

                    5847 (14.4)

                    6606 (11.3)

                    Blood Transfusion

                    3391 (8.3)

                    3158 (5.4)

                    MTCT

                    510 (1.3)

                    677 (1.2)

                    MSM

                    453 (1.1)

                    487 (0.8)

                    Sex (both MSM and heterosexual ) + IDU

                    283 (0.7)

                    452 (0.8)

                    Unknown

                    /

                    8371 (14.4)

                    Location of death

                      

                    Local county

                    25309 (61.9)

                    35523 (60.9)

                    Local city and other county

                    8116 (19.9)

                    10671 (18.3)

                    Local province and other city

                    5507 (13.5)

                    9258 (15.9)

                    Other province

                    1914 (4.7)

                    2865 (4.9)

                    Reporting units

                      

                    Disease control system

                    24833 (61.0)

                    39862 (68.4)

                    Medical institution

                    15744 (38.6)

                    17746 (30.4)

                    Blood center

                    26 (0.1)

                    105 (0.2)

                    Drug addiction treatment facility

                    53 (0.1)

                    472 (0.8)

                    Occupation

                      

                    Farmer

                    24411 (59.9)

                    31839 (54.6)

                    Housekeeper and unemployed

                    4556 (11.2)

                    9039 (15.5)

                    Laborer

                    2083 (5.1)

                    2784 (4.8)

                    Retired

                    1392 (3.4)

                    1528 (2.6)

                    Migrant worker

                    1308 (3.2)

                    1522 (2.6)

                    Business

                    /

                    1346 (2.3)

                    Underlying cause of death

                    Out of 72,616 total deaths, there were 63,785 cases with information on cause of death, of which 31,223 (49.0%) were considered AIDS-related deaths and 32,562 had other causes of death. AIDS deaths accounted for 57.1% of all deaths after cause of death was verified. Results are shown in Table  5.
                    Table 5

                    Cause of death for HIV cases by the end of 2010

                    Causes of death

                    Original report

                    After adjustment

                    Number of cases

                    %

                    Number of cases

                    %

                    AIDS

                    31223

                    49.0

                    36438

                    57.1

                    Other

                    32562

                    51.1

                    27347

                    42.9

                    Total

                    63785

                    100.0

                    63785

                    100.0

                    Characteristics of treated deaths by baseline CD4 cell counts

                    Based on the data collected from January 1, 2011 to July 31, 2011, most of the patients who died without initiating HAART died either more than 12 months from diagnosis (n = 2,027) or less than three months from diagnosis (n = 2,543). More than half (57.5%) never received a CD4 test before their death. Among the 2,375 patients who had received a CD4 test, 1,772 had first CD4 test results that met the criteria to initiate treatment. Among 603 patients with CD4 counts >350 cells/μL, 210 (34.8%) had a subsequent CD4 test with a count < 350 cells/μL (Table  6).
                    Table 6

                    CD4 test results for mortality cases from January 1, 2011 to July 31, 2011

                    Interval between death and confirming (months)

                    Number of deaths

                    Proportion with at least one CD4 test N (%)

                    First CD4 test results

                    <50

                    50-200

                    201-350

                    >350

                    0-2

                    2543

                    567 (22.3%)

                    360

                    145

                    28

                    34

                    3-5

                    457

                    245 (53.6%)

                    124

                    83

                    18

                    20

                    6-8

                    334

                    189 (56.6%)

                    82

                    62

                    15

                    30

                    9-11

                    222

                    120 (54.1%)

                    33

                    47

                    11

                    29

                    ≥12

                    2027

                    1254 (61.9%)

                    173

                    274

                    317

                    490

                    Total

                    5583

                    2375 (42.5%)

                    772

                    611

                    389

                    603

                    The probability of survival after receiving HAART is shown by the Kaplan-Meier method in Figure  1. The study population was analyzed in four groups based on baseline CD4 cell counts (Group 1: CD4 count <50 cells/μL, Group 2: CD4 count ≥50 cells/μL and ≤200 cells/μL, Group 3: CD4 count >200 cells/μL and ≤350 cells/μL, and Group 4: CD4 count > 350 cells/μL). Survival rates at 12, 36, and 60 days were 82.1%, 75.9%, and 70.9% in Group 1; 92.6%, 85.8%, and 82.7% in Group 2; 95.9%, 89.6% and 85.6% in Group 3; and 96.4%, 90.8% and 87.4% in Group 4 (log-rank test, p <0.001).
                    http://static-content.springer.com/image/art%3A10.1186%2F1471-2458-14-831/MediaObjects/12889_2014_6948_Fig1_HTML.jpg
                    Figure 1

                    The survival probability for cases receiving antiretroviral therapy, arranged by baseline CD4 cell count.

                    Discussion

                    This study showed some notable findings on causes of death among a large and demographically diverse population of HIV patients. Mortality was higher among those patients aged 50 years old or older who had been infected by IDU or blood transfusion/donation and had not accepted HAART.

                    According to our study, the risk factor strongly related to death was age, especially for individuals aged 50 years old or older. There may be many reasons for this. First of all, a substantial proportion of elderly people who meet treatment criteria may give up treatment voluntarily. This is likely to be associated with the personal characteristics of this group, such as having low levels of education and desiring to reduce the burden on family. Furthermore, individuals in older age groups may be likely to refuse treatment as a result of the stigma associated with HIV/AIDS [27], without the support of social and family members. Last but not least, elderly people may be underserved by the public health system, especially in some less developed areas.

                    The study noted a significant risk of mortality in individuals who were infected by IDU and blood transfusion/donation compared to heterosexual transmission. IDU was the primary mean of transmission at the beginning of China’s HIV epidemic [28], which led to a long period of infection and late treatment initiation. Consequently, those infected by IDU had a higher mortality rate, which is consistent with other reports [29, 30]. In addition, the high mortality in IDUs may be associated with personal characteristics; some foreign studies indicated that the majority of accidental deaths among IDUs are from drug overdoses [31, 32].

                    Similarly, blood transfusion was also one of the main routes in the early phase of China [33], most former plasma donors (FPD) or blood receptors infected by HIV long time ago, which made the disease developed long enough to become a danger for bodies and thus a risk factor for higher mortality [34].

                    Significantly, there is a protective influence in individuals infected by homosexual transmission, in contrast with individuals infected by heterosexual transmission. There may be a bias here due to the database’s small sample of men who have sex with men (MSM), although the population of MSM has begun to increase in recent years in China [35, 36]. Another potential reason may be that the majority of MSM are relatively young.

                    The results of our study reveal a statistically significant difference between accepting HAART or not. The results provide evidence that increasing HAART coverage at the population level can decrease HIV-related mortality, which conforms with the results of overseas findings such as those from the mid-to-late 1990s in the USA [4], Europe [37] and in other earlier studies [3840].

                    In the five provinces with the highest proportion of cumulative AIDS deaths, patients who died almost always had a history of paid blood donation. These regions excelled at early detection, management of cases and follow-ups, and reporting deaths. Conversely, in Beijing, Shanghai, Tianjin, and Zhejiang, the proportion of cumulative AIDS deaths was the lowest among all provinces. The reason for the low AIDS deaths in these areas may be due to their large migrant populations, whose high mobility complicates follow-up.

                    According to previous studies, end-stage patients with low CD4 counts achieve significantly fewer life-prolonging effects through HAART than those with high CD4 counts. However, according to the results of this study, most of those who died before initiating HAART never had a CD4 test. Efforts should be made to improve coverage of HIV diagnostic tests and the frequency of CD4 testing in order to offer timely HAART to prolong survival time. The median time between diagnosis and death was only 0.7 years, and nearly half of cases were discovered late. Though HAART can effectively reduce the fatality rate of HIV/AIDS, many at-risk individuals do not seek out standard HIV counseling and testing services. The stigmas associated with drug use and HIV/AIDS and the fear of arrest or of a positive result can be major barriers to accessing HIV voluntary counseling and testing (VCT) services [41]. It is critical to scale up early monitoring to provide prompt treatment and effectively reduce AIDS mortality.

                    The results of this survival analysis indicate the benefits of HAART in reducing overall mortality and AIDS-related morbidity, which is similar to results in other studies [42, 43]. However, the cumulative number of HIV-positive adults using HAART in China was less than 20% by the end of 2010 [20]. The results indicate that HIV-positive individuals need to be diagnosed much earlier, which would suggest that HIV testing programs should be expanded.

                    In our study, survival analysis in HIV patients received HAART also indicated that individuals were more likely to have a long interval of time between diagnosis and death compared to individuals who had high baseline CD4 cell counts. The differences were more visible between Group 1 and Group 4. Timely HAART should be provided to prolong survival time, as receiving HAART is the best way to reduce mortality. Mechanisms should be in place to prevent the development of drug resistance and to enhance clinical services, including implementing viral load testing, increasing adherence, and providing prompt second-line therapy for patients with first-line treatment failure.

                    This study had several limitations. First, data were used from sentinel detection databases and may not be representative of all deaths in China, may exclude those who were homeless or living alone when they died and may underestimate AIDS-related mortality. Second, many could have died of AIDS, but if they were never diagnosed the cause of death could have been listed as something else and they would not be included in the databases. Third, data may have been missing from the databases for other reasons. Missing data may influence the determination of receiving HAART or not, which likely underestimates the proportion of patients who had initiated HAART.

                    Conclusions

                    In summary, early diagnosis of HIV can maximize the effectiveness of HAART. It is essential to continue monitoring HAART uptake and adherence in China, which will help to save lives.

                    Abbreviations

                    HAART: 

                    Highly active antiretroviral therapy

                    NFATP: 

                    National Free Antiretroviral Treatment Programme

                    OI: 

                    Opportunistic infections

                    PCP: 

                    Pneumocystis pneumonia

                    IDU: 

                    Injection drug use

                    MSM: 

                    Men who have sex with men

                    VCT: 

                    Voluntary counseling and testing.

                    Declarations

                    Acknowledgements

                    The authors are grateful to Professor Rongbin Yu for important suggestions on this research. This work was funded by Natural Science Foundation of China (81001288), National S&T Major Project Foundation of China (No. 2012ZX10001-001 and No. 2011ZX10004-902), Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD), Jiangsu Province Health Development Project with Science and Education (NO.ZX201109), and National Science and Technology Support Program (2011BAI09B02). The comments and suggestions from the reviewers are also deeply appreciated.

                    Authors’ Affiliations

                    (1)
                    Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University
                    (2)
                    National Center for AIDS/STD Control and Prevention, Chinese Center for Disease Control and Prevention

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                    44. Pre-publication history

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