Do health sector measures of violence against women at different levels of severity correlate? Evidence from Brazil | BMC Women’s Health

Data sources

Health data on violence against women come from three sources. For each source, I consider only females ages 15–49.

Vital statistics data on homicides are from Brazil’s National mortality database Sistema de Informações de Mortalidade (SIM—System of Mortality Information) [14], a registry of every death in the nation. I consider a death to be a homicide if it is coded to be assault (i.e., codes ICD-10 X85-Y09) or if it was categorized as a homicide. The discrepancy between these definitions is only 6%.

The Sistema de Comunicação de Informação de Hospitalar e Ambulatorial (CIHA—System of Communication of Hospital and Outpatient Information) [15] includes information from public and private hospitals. Hospitalizations are recorded when the physician fills out a form called Autorização de Internação Hospitalar (AIH—Authorization for Hospitalization), which is used to reimburse the hospital for the procedures performed on its patients. This data only captures the most severe incidents because, according to the Ministry of Health, this form is filled out when patients need to be admitted overnight. I exclude hospitalizations that resulted in deaths, as these would be duplicates with the SIM death registry. The reason for intake is classified with the ICD-10, which I use to select the hospitalizations for assault using the same codes as in the homicide data (X85-Y09). Though the primary cause typically indicates the location of the injury (e.g. trauma of the knee or leg), the secondary cause often suggests the reason for the injury (e.g. accident, assault).

There are two concerns regarding hospitalization data accuracy. The AIH form is also filled out when patients need a procedure, so some non-overnight patients may also be in the system. However, few of those procedures are associated with assault and thus are unlikely to bias our results. The other concern is that hospitals cannot report more patients than the number of beds they have registered at the centralized health care system. Thus, the system may be under-reporting incidents in crowded hospitals.

The Sistema de Informação de Agravos de Notificação (SINAN—Notifiable Diseases Information System) are reports filled out at the health care units by the attending medical personnel (e.g. doctor, nurse, dentist, psychologist, social worker). These reports are mandatory for cases of domestic violence (including IPV) and violence against women, children, and elderly [16]. However, the follow-through of contacting police or social workers, for example, is only required for children and elderly, though implementation of federal law locally can vary widely across the country. Although psychological, financial, and sexual violence can be reported, I only include SINAN cases for which sexual and physical violence was reported, since these correspond to the assault categories of the ICD-10 codes used in the homicide and hospitalization data. I divided the SINAN data into less severe and more severe reports. The more severe reports were those from hospitals and emergency rooms while the less severe were those reported by clinics and other establishments. I also considered the subset of data in which the perpetrator was identified as partner or ex-partner; I add the qualifier “IPV” to this subsample. This subsample includes both clinic reports and hospital reports to maintain a larger sample size.

There may be some noise in how incidents were classified. Depending on availability of clinics or the hours they were open, some women may have used hospital emergency rooms for less severe incidents. Similarly, clinics may refer women with more severe injuries to hospitals. Unfortunately, this information is unknown in the data and has to be considered noise for the purposes of this analysis. While the overnight hospitalizations (and perhaps even a few deaths) may appear in the SINAN reports of hospitalizations, the magnitude difference is so large that the overnight hospitalizations would only be a very small part of the noise in the SINAN reports from hospitals. Even if reported in the SINAN reports, women who were hospitalized overnight for aggression or were murdered should still appear in the CIHA and SIM registries as these are registries are for different purposes than the SINAN reports. Additionally, I excluded any overnight hospitalizations that resulted in death from that categorization, so there should be minimal noise in that regard to overlap between the overnight hospitalization and homicide categories.

Several municipal measures from 2011 relating to policy that may influence IPV or IPV reporting are used in the analysis: a poverty measure, if the municipality has a woman’s police station, if the municipality has a local civil police station which investigates other crimes (as opposed to another municipality being in charge of investigations), police investment, health investment, civic engagement, the share of population that is female, and population size. For a number of these measures, I used exploratory factor analysis to create an index of several correlated variables. A poverty measure was created from measures of Bolsa Familia coverage (portion of eligible women) and Bolsa Familia transfer per woman from data from the Ministerio de Desenvolvimento Social (MDS—Ministry of Social Development) [17], combined with the human development index [18]. Using data from the Brazilian Treasury (Finanças do Brasil—FINBRA) [19, 20], a police investment index was created from per capita spending on policing and per capita spending on public safety. A health investment index was created from per capita spending on clinics, per capita spending on health establishments, per capita spending on hospital beds, per capita health spending in general, per capita spending on hospital aid, and per capita spending on social assistance. Finally, from the Survey of Basic Municipal Information (MUNIC) [21], a civic engagement measure was created from variables indicating if the municipality had a safety council, a health council and a human rights council.

Sample size and definition

For the correlation analysis, I use quarterly observations, for a total of 24 periods. I select cities that have a high volume of incidents reported to DataSus: I only used municipalities in which all outcomes had observations in at least half of the quarters. This restriction reduces inflated correlation due to zeros being correlated with zeros. In this longitudinal analysis, I limit SINAN reports to facilities that were already reporting in 2011—the first year the system was nationally required—to avoid bias due to an increase in reporting rather than a change in incidence. (This approach has been used elsewhere [13]). Brasilia, the federal capital, was excluded due to having more national level influences than other municipalities and I also eliminated two municipalities that were missing data on poverty, resulting in a total of 63 municipalities. Though these 63 municipalities are only about 1% of the municipalities reporting violence, they include 30% of the population of women ages 15–49 and around a fifth of the incidents.

Analysis

I contrast distributions of victims in the different reporting systems by age, race, and education. Education data is not available for hospitalizations. For reports and homicides, I also examine time of day of the incident and if the incident occurred at home or not. For the reports, additional information was available on if these incidents were single events or multiple events, if a weapon was involved, and the identity of the perpetrator. Weapons were broadly defined and could include household objects or sticks, for example, in addition to knives and firearms.

I test three hypotheses. First, that violence against women is correlated at different levels of severity. I aggregate the data to get a count of the incidence in each municipality for each quarter from the period 2011–2016. Then, for each municipality, I calculate the correlation of the quarterly incidents: reports from clinics, reports from emergency rooms and hospitals, overnight hospitalizations, and homicides. I use a t-test to determine if the average correlation between each of these groups is distinct from zero. Using the same method, I test the hypothesis that IPV reports are associated with hospitalizations and homicides. Finally, I test the hypothesis that municipal characteristics predict heterogeneity in the correlations. I use OLS multiple regression to examine if municipal characteristics explain why within some municipalities’ measures of violence are more correlated than in others.